Science.gov

Sample records for network information theory

  1. Information theory perspective on network robustness

    NASA Astrophysics Data System (ADS)

    Schieber, Tiago A.; Carpi, Laura; Frery, Alejandro C.; Rosso, Osvaldo A.; Pardalos, Panos M.; Ravetti, Martín G.

    2016-01-01

    A crucial challenge in network theory is the study of the robustness of a network when facing a sequence of failures. In this work, we propose a dynamical definition of network robustness based on Information Theory, that considers measurements of the structural changes caused by failures of the network's components. Failures are defined here as a temporal process defined in a sequence. Robustness is then evaluated by measuring dissimilarities between topologies after each time step of the sequence, providing a dynamical information about the topological damage. We thoroughly analyze the efficiency of the method in capturing small perturbations by considering different probability distributions on networks. In particular, we find that distributions based on distances are more consistent in capturing network structural deviations, as better reflect the consequences of the failures. Theoretical examples and real networks are used to study the performance of this methodology.

  2. Information theory and the ethylene genetic network

    PubMed Central

    González-García, José S

    2011-01-01

    The original aim of the Information Theory (IT) was to solve a purely technical problem: to increase the performance of communication systems, which are constantly affected by interferences that diminish the quality of the transmitted information. That is, the theory deals only with the problem of transmitting with the maximal precision the symbols constituting a message. In Shannon's theory messages are characterized only by their probabilities, regardless of their value or meaning. As for its present day status, it is generally acknowledged that Information Theory has solid mathematical foundations and has fruitful strong links with Physics in both theoretical and experimental areas. However, many applications of Information Theory to Biology are limited to using it as a technical tool to analyze biopolymers, such as DNA, RNA or protein sequences. The main point of discussion about the applicability of IT to explain the information flow in biological systems is that in a classic communication channel, the symbols that conform the coded message are transmitted one by one in an independent form through a noisy communication channel, and noise can alter each of the symbols, distorting the message; in contrast, in a genetic communication channel the coded messages are not transmitted in the form of symbols but signaling cascades transmit them. Consequently, the information flow from the emitter to the effector is due to a series of coupled physicochemical processes that must ensure the accurate transmission of the message. In this review we discussed a novel proposal to overcome this difficulty, which consists of the modeling of gene expression with a stochastic approach that allows Shannon entropy (H) to be directly used to measure the amount of uncertainty that the genetic machinery has in relation to the correct decoding of a message transmitted into the nucleus by a signaling pathway. From the value of H we can define a function I that measures the amount of

  3. Information theory and the ethylene genetic network.

    PubMed

    González-García, José S; Díaz, José

    2011-10-01

    The original aim of the Information Theory (IT) was to solve a purely technical problem: to increase the performance of communication systems, which are constantly affected by interferences that diminish the quality of the transmitted information. That is, the theory deals only with the problem of transmitting with the maximal precision the symbols constituting a message. In Shannon's theory messages are characterized only by their probabilities, regardless of their value or meaning. As for its present day status, it is generally acknowledged that Information Theory has solid mathematical foundations and has fruitful strong links with Physics in both theoretical and experimental areas. However, many applications of Information Theory to Biology are limited to using it as a technical tool to analyze biopolymers, such as DNA, RNA or protein sequences. The main point of discussion about the applicability of IT to explain the information flow in biological systems is that in a classic communication channel, the symbols that conform the coded message are transmitted one by one in an independent form through a noisy communication channel, and noise can alter each of the symbols, distorting the message; in contrast, in a genetic communication channel the coded messages are not transmitted in the form of symbols but signaling cascades transmit them. Consequently, the information flow from the emitter to the effector is due to a series of coupled physicochemical processes that must ensure the accurate transmission of the message. In this review we discussed a novel proposal to overcome this difficulty, which consists of the modeling of gene expression with a stochastic approach that allows Shannon entropy (H) to be directly used to measure the amount of uncertainty that the genetic machinery has in relation to the correct decoding of a message transmitted into the nucleus by a signaling pathway. From the value of H we can define a function I that measures the amount of

  4. Information theory and the ethylene genetic network.

    PubMed

    González-García, José S; Díaz, José

    2011-10-01

    The original aim of the Information Theory (IT) was to solve a purely technical problem: to increase the performance of communication systems, which are constantly affected by interferences that diminish the quality of the transmitted information. That is, the theory deals only with the problem of transmitting with the maximal precision the symbols constituting a message. In Shannon's theory messages are characterized only by their probabilities, regardless of their value or meaning. As for its present day status, it is generally acknowledged that Information Theory has solid mathematical foundations and has fruitful strong links with Physics in both theoretical and experimental areas. However, many applications of Information Theory to Biology are limited to using it as a technical tool to analyze biopolymers, such as DNA, RNA or protein sequences. The main point of discussion about the applicability of IT to explain the information flow in biological systems is that in a classic communication channel, the symbols that conform the coded message are transmitted one by one in an independent form through a noisy communication channel, and noise can alter each of the symbols, distorting the message; in contrast, in a genetic communication channel the coded messages are not transmitted in the form of symbols but signaling cascades transmit them. Consequently, the information flow from the emitter to the effector is due to a series of coupled physicochemical processes that must ensure the accurate transmission of the message. In this review we discussed a novel proposal to overcome this difficulty, which consists of the modeling of gene expression with a stochastic approach that allows Shannon entropy (H) to be directly used to measure the amount of uncertainty that the genetic machinery has in relation to the correct decoding of a message transmitted into the nucleus by a signaling pathway. From the value of H we can define a function I that measures the amount of

  5. Analyzing complex networks evolution through Information Theory quantifiers

    NASA Astrophysics Data System (ADS)

    Carpi, Laura C.; Rosso, Osvaldo A.; Saco, Patricia M.; Ravetti, Martín Gómez

    2011-01-01

    A methodology to analyze dynamical changes in complex networks based on Information Theory quantifiers is proposed. The square root of the Jensen-Shannon divergence, a measure of dissimilarity between two probability distributions, and the MPR Statistical Complexity are used to quantify states in the network evolution process. Three cases are analyzed, the Watts-Strogatz model, a gene network during the progression of Alzheimer's disease and a climate network for the Tropical Pacific region to study the El Niño/Southern Oscillation (ENSO) dynamic. We find that the proposed quantifiers are able not only to capture changes in the dynamics of the processes but also to quantify and compare states in their evolution.

  6. Methods of information theory and algorithmic complexity for network biology.

    PubMed

    Zenil, Hector; Kiani, Narsis A; Tegnér, Jesper

    2016-03-01

    We survey and introduce concepts and tools located at the intersection of information theory and network biology. We show that Shannon's information entropy, compressibility and algorithmic complexity quantify different local and global aspects of synthetic and biological data. We show examples such as the emergence of giant components in Erdös-Rényi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression data. We provide exact theoretical calculations, numerical approximations and error estimations of entropy, algorithmic probability and Kolmogorov complexity for different types of graphs, characterizing their variant and invariant properties. We introduce formal definitions of complexity for both labeled and unlabeled graphs and prove that the Kolmogorov complexity of a labeled graph is a good approximation of its unlabeled Kolmogorov complexity and thus a robust definition of graph complexity.

  7. Information theory in systems biology. Part I: Gene regulatory and metabolic networks.

    PubMed

    Mousavian, Zaynab; Kavousi, Kaveh; Masoudi-Nejad, Ali

    2016-03-01

    "A Mathematical Theory of Communication", was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein-protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory.

  8. Information theory and signal transduction systems: from molecular information processing to network inference.

    PubMed

    Mc Mahon, Siobhan S; Sim, Aaron; Filippi, Sarah; Johnson, Robert; Liepe, Juliane; Smith, Dominic; Stumpf, Michael P H

    2014-11-01

    Sensing and responding to the environment are two essential functions that all biological organisms need to master for survival and successful reproduction. Developmental processes are marshalled by a diverse set of signalling and control systems, ranging from systems with simple chemical inputs and outputs to complex molecular and cellular networks with non-linear dynamics. Information theory provides a powerful and convenient framework in which such systems can be studied; but it also provides the means to reconstruct the structure and dynamics of molecular interaction networks underlying physiological and developmental processes. Here we supply a brief description of its basic concepts and introduce some useful tools for systems and developmental biologists. Along with a brief but thorough theoretical primer, we demonstrate the wide applicability and biological application-specific nuances by way of different illustrative vignettes. In particular, we focus on the characterisation of biological information processing efficiency, examining cell-fate decision making processes, gene regulatory network reconstruction, and efficient signal transduction experimental design.

  9. Information theory in systems biology. Part II: protein-protein interaction and signaling networks.

    PubMed

    Mousavian, Zaynab; Díaz, José; Masoudi-Nejad, Ali

    2016-03-01

    By the development of information theory in 1948 by Claude Shannon to address the problems in the field of data storage and data communication over (noisy) communication channel, it has been successfully applied in many other research areas such as bioinformatics and systems biology. In this manuscript, we attempt to review some of the existing literatures in systems biology, which are using the information theory measures in their calculations. As we have reviewed most of the existing information-theoretic methods in gene regulatory and metabolic networks in the first part of the review, so in the second part of our study, the application of information theory in other types of biological networks including protein-protein interaction and signaling networks will be surveyed.

  10. The use of network theory to model disparate ship design information

    NASA Astrophysics Data System (ADS)

    Rigterink, Douglas; Piks, Rebecca; Singer, David J.

    2014-06-01

    This paper introduces the use of network theory to model and analyze disparate ship design information. This work will focus on a ship's distributed systems and their intra- and intersystem structures and interactions. The three system to be analyzed are: a passageway system, an electrical system, and a fire fighting system. These systems will be analyzed individually using common network metrics to glean information regarding their structures and attributes. The systems will also be subjected to community detection algorithms both separately and as a multiplex network to compare their similarities, differences, and interactions. Network theory will be shown to be useful in the early design stage due to its simplicity and ability to model any shipboard system.

  11. Application of immune network theory for target-oriented multi-spectral remote sensing information mining

    NASA Astrophysics Data System (ADS)

    Liu, Qing-jie; Lin, Qi-zhong

    2008-12-01

    To use target information for space transformation in remote sensing data field, artificial immune network theory is introduced to multi-spectral remote sensing information mining, based on the knowledge of target spectrum. First, the target spectrums are fuzzy clustered into several subclasses, to retain different features of target in different subclasses. Then we develop a novel Regional-memory-pattern Artificial Immune Idiotypic Network (RAIN) model based on artificial idiotypic network theory, and train RAIN with subclasses samples. And then, the affinities of the target spectrum and other objects can be calculated according to the immune microscopic dynamics including stimulation and suppression effect. Finally, principal component analysis (PCA) is performed to affinities to explore more weak and hidden information. With its application in Baoguto Area, Xinjiang Uyghur Autonomous Region China, choosing tuffaceous siltstone as target object, the result supports the efficiency of the RAIN-affinity-PCA scheme.

  12. Protein signaling networks from single cell fluctuations and information theory profiling.

    PubMed

    Shin, Young Shik; Remacle, F; Fan, Rong; Hwang, Kiwook; Wei, Wei; Ahmad, Habib; Levine, R D; Heath, James R

    2011-05-18

    Protein signaling networks among cells play critical roles in a host of pathophysiological processes, from inflammation to tumorigenesis. We report on an approach that integrates microfluidic cell handling, in situ protein secretion profiling, and information theory to determine an extracellular protein-signaling network and the role of perturbations. We assayed 12 proteins secreted from human macrophages that were subjected to lipopolysaccharide challenge, which emulates the macrophage-based innate immune responses against Gram-negative bacteria. We characterize the fluctuations in protein secretion of single cells, and of small cell colonies (n = 2, 3,···), as a function of colony size. Measuring the fluctuations permits a validation of the conditions required for the application of a quantitative version of the Le Chatelier's principle, as derived using information theory. This principle provides a quantitative prediction of the role of perturbations and allows a characterization of a protein-protein interaction network. PMID:21575571

  13. Analysis of Online Social Networks to Understand Information Sharing Behaviors Through Social Cognitive Theory

    SciTech Connect

    Yoon, Hong-Jun; Tourassi, Georgia

    2014-01-01

    Analyzing the contents of online social networks is an effective process for monitoring and understanding peoples behaviors. Since the nature of conversation and information propagation is similar to traditional conversation and learning, one of the popular socio-cognitive methods, social cognitive theory was applied to online social networks to. Two major news topics about colon cancer were chosen to monitor traffic of Twitter messages. The activity of leaders on the issue (i.e., news companies or people will prior Twitter activity on topics related to colon cancer) was monitored. In addition, the activity of followers , people who never discussed the topics before, but replied to the discussions was also monitored. Topics that produce tangible benefits such as positive outcomes from appropriate preventive actions received dramatically more attention and online social media traffic. Such characteristics can be explained with social cognitive theory and thus present opportunities for effective health campaigns.

  14. Optimal design of hydrometric monitoring networks with dynamic components based on Information Theory

    NASA Astrophysics Data System (ADS)

    Alfonso, Leonardo; Chacon, Juan; Solomatine, Dimitri

    2016-04-01

    The EC-FP7 WeSenseIt project proposes the development of a Citizen Observatory of Water, aiming at enhancing environmental monitoring and forecasting with the help of citizens equipped with low-cost sensors and personal devices such as smartphones and smart umbrellas. In this regard, Citizen Observatories may complement the limited data availability in terms of spatial and temporal density, which is of interest, among other areas, to improve hydraulic and hydrological models. At this point, the following question arises: how can citizens, who are part of a citizen observatory, be optimally guided so that the data they collect and send is useful to improve modelling and water management? This research proposes a new methodology to identify the optimal location and timing of potential observations coming from moving sensors of hydrological variables. The methodology is based on Information Theory, which has been widely used in hydrometric monitoring design [1-4]. In particular, the concepts of Joint Entropy, as a measure of the amount of information that is contained in a set of random variables, which, in our case, correspond to the time series of hydrological variables captured at given locations in a catchment. The methodology presented is a step forward in the state of the art because it solves the multiobjective optimisation problem of getting simultaneously the minimum number of informative and non-redundant sensors needed for a given time, so that the best configuration of monitoring sites is found at every particular moment in time. To this end, the existing algorithms have been improved to make them efficient. The method is applied to cases in The Netherlands, UK and Italy and proves to have a great potential to complement the existing in-situ monitoring networks. [1] Alfonso, L., A. Lobbrecht, and R. Price (2010a), Information theory-based approach for location of monitoring water level gauges in polders, Water Resour. Res., 46(3), W03528 [2] Alfonso, L., A

  15. Parametric sensitivity analysis for biochemical reaction networks based on pathwise information theory

    PubMed Central

    2013-01-01

    Background Stochastic modeling and simulation provide powerful predictive methods for the intrinsic understanding of fundamental mechanisms in complex biochemical networks. Typically, such mathematical models involve networks of coupled jump stochastic processes with a large number of parameters that need to be suitably calibrated against experimental data. In this direction, the parameter sensitivity analysis of reaction networks is an essential mathematical and computational tool, yielding information regarding the robustness and the identifiability of model parameters. However, existing sensitivity analysis approaches such as variants of the finite difference method can have an overwhelming computational cost in models with a high-dimensional parameter space. Results We develop a sensitivity analysis methodology suitable for complex stochastic reaction networks with a large number of parameters. The proposed approach is based on Information Theory methods and relies on the quantification of information loss due to parameter perturbations between time-series distributions. For this reason, we need to work on path-space, i.e., the set consisting of all stochastic trajectories, hence the proposed approach is referred to as “pathwise”. The pathwise sensitivity analysis method is realized by employing the rigorously-derived Relative Entropy Rate, which is directly computable from the propensity functions. A key aspect of the method is that an associated pathwise Fisher Information Matrix (FIM) is defined, which in turn constitutes a gradient-free approach to quantifying parameter sensitivities. The structure of the FIM turns out to be block-diagonal, revealing hidden parameter dependencies and sensitivities in reaction networks. Conclusions As a gradient-free method, the proposed sensitivity analysis provides a significant advantage when dealing with complex stochastic systems with a large number of parameters. In addition, the knowledge of the structure of the

  16. Blogs and Social Network Sites as Activity Systems: Exploring Adult Informal Learning Process through Activity Theory Framework

    ERIC Educational Resources Information Center

    Heo, Gyeong Mi; Lee, Romee

    2013-01-01

    This paper uses an Activity Theory framework to explore adult user activities and informal learning processes as reflected in their blogs and social network sites (SNS). Using the assumption that a web-based space is an activity system in which learning occurs, typical features of the components were investigated and each activity system then…

  17. Actor-Network Theory and its role in understanding the implementation of information technology developments in healthcare

    PubMed Central

    2010-01-01

    Background Actor-Network Theory (ANT) is an increasingly influential, but still deeply contested, approach to understand humans and their interactions with inanimate objects. We argue that health services research, and in particular evaluations of complex IT systems in health service organisations, may benefit from being informed by Actor-Network Theory perspectives. Discussion Despite some limitations, an Actor-Network Theory-based approach is conceptually useful in helping to appreciate the complexity of reality (including the complexity of organisations) and the active role of technology in this context. This can prove helpful in understanding how social effects are generated as a result of associations between different actors in a network. Of central importance in this respect is that Actor-Network Theory provides a lens through which to view the role of technology in shaping social processes. Attention to this shaping role can contribute to a more holistic appreciation of the complexity of technology introduction in healthcare settings. It can also prove practically useful in providing a theoretically informed approach to sampling (by drawing on informants that are related to the technology in question) and analysis (by providing a conceptual tool and vocabulary that can form the basis for interpretations). We draw on existing empirical work in this area and our ongoing work investigating the integration of electronic health record systems introduced as part of England's National Programme for Information Technology to illustrate salient points. Summary Actor-Network Theory needs to be used pragmatically with an appreciation of its shortcomings. Our experiences suggest it can be helpful in investigating technology implementations in healthcare settings. PMID:21040575

  18. Intervening in Partner Violence against Women: A Grounded Theory Exploration of Informal Network Members' Experiences

    ERIC Educational Resources Information Center

    Latta, Rachel E.; Goodman, Lisa A.

    2011-01-01

    A large body of cross-sectional and longitudinal research demonstrates the important contribution of informal social networks to the well-being and safety of female survivors of intimate partner violence (IPV). Most survivors turn to family and friends before, during, and after their involvement with formal services; and many rely solely on…

  19. Information theory and local learning rules in a self-organizing network of Ising spins

    NASA Astrophysics Data System (ADS)

    Haft, Michael; Schlang, Martin; Deco, Gustavo

    1995-09-01

    The Boltzmann machine uses the relative entropy as a cost function to fit the Boltzmann distribution to a fixed given distribution. Instead of the relative entropy, we use the mutual information between input and output units to define an unsupervised analogy to the conventional Boltzmann machine. Our network of Ising spins is fed by an external field via the input units. The output units should self-organize to form an ``internal'' representation of the ``environmental'' input, thereby compressing the data and extracting relevant features. The mutual information and its gradient with respect to the weights principally require nonlocal information, e.g., in the form of multipoint correlation functions. Hence the exact gradient can hardly be boiled down to a local learning rule. Conversely, by using only local terms and two-point interactions, the entropy of the output layer cannot be ensured to reach the maximum possible entropy for a fixed number of output neurons. Some redundancy may remain in the representation of the data at the output. We account for this limitation from the very beginning by reformulating the cost function correspondingly. From this cost function, local Hebb-like learning rules can be derived. Some experiments with these local learning rules are presented.

  20. Optimal Observation Network Design for Model Discrimination using Information Theory and Bayesian Model Averaging

    NASA Astrophysics Data System (ADS)

    Pham, H. V.; Tsai, F. T. C.

    2014-12-01

    Groundwater systems are complex and subject to multiple interpretations and conceptualizations due to a lack of sufficient information. As a result, multiple conceptual models are often developed and their mean predictions are preferably used to avoid biased predictions from using a single conceptual model. Yet considering too many conceptual models may lead to high prediction uncertainty and may lose the purpose of model development. In order to reduce the number of models, an optimal observation network design is proposed based on maximizing the Kullback-Leibler (KL) information to discriminate competing models. The KL discrimination function derived by Box and Hill [1967] for one additional observation datum at a time is expanded to account for multiple independent spatiotemporal observations. The Bayesian model averaging (BMA) method is used to incorporate existing data and quantify future observation uncertainty arising from conceptual and parametric uncertainties in the discrimination function. To consider the future observation uncertainty, the Monte Carlo realizations of BMA predicted future observations are used to calculate the mean and variance of posterior model probabilities of the competing models. The goal of the optimal observation network design is to find the number and location of observation wells and sampling rounds such that the highest posterior model probability of a model is larger than a desired probability criterion (e.g., 95%). The optimal observation network design is implemented to a groundwater study in the Baton Rouge area, Louisiana to collect new groundwater heads from USGS wells. The considered sources of uncertainty that create multiple groundwater models are the geological architecture, the boundary condition, and the fault permeability architecture. All possible design solutions are enumerated using high performance computing systems. Results show that total model variance (the sum of within-model variance and between

  1. Complex-disease networks of trait-associated single-nucleotide polymorphisms (SNPs) unveiled by information theory

    PubMed Central

    Li, Haiquan; Lee, Younghee; Chen, James L; Rebman, Ellen; Li, Jianrong

    2012-01-01

    Objective Thousands of complex-disease single-nucleotide polymorphisms (SNPs) have been discovered in genome-wide association studies (GWAS). However, these intragenic SNPs have not been collectively mined to unveil the genetic architecture between complex clinical traits. The authors hypothesize that biological annotations of host genes of trait-associated SNPs may reveal the biomolecular modularity across complex-disease traits and offer insights for drug repositioning. Methods Trait-to-polymorphism (SNPs) associations confirmed in GWAS were used. A novel method to quantify trait–trait similarity anchored in Gene Ontology annotations of human proteins and information theory was developed. The results were then validated with the shortest paths of physical protein interactions between biologically similar traits. Results A network was constructed consisting of 280 significant intertrait similarities among 177 disease traits, which covered 1438 well-validated disease-associated SNPs. Thirty-nine percent of intertrait connections were confirmed by curators, and the following additional studies demonstrated the validity of a proportion of the remainder. On a phenotypic trait level, higher Gene Ontology similarity between proteins correlated with smaller ‘shortest distance’ in protein interaction networks of complexly inherited diseases (Spearman p<2.2×10−16). Further, ‘cancer traits’ were similar to one another, as were ‘metabolic syndrome traits’ (Fisher's exact test p=0.001 and 3.5×10−7, respectively). Conclusion An imputed disease network by information-anchored functional similarity from GWAS trait-associated SNPs is reported. It is also demonstrated that small shortest paths of protein interactions correlate with complex-disease function. Taken together, these findings provide the framework for investigating drug targets with unbiased functional biomolecular networks rather than worn-out single-gene and subjective canonical pathway approaches

  2. Information Networks: Definitions and Message Transfer Models.

    ERIC Educational Resources Information Center

    Nance, Richard E.; And Others

    A mathematical definition of an information network is constructed with the purpose of developing a theory useful in answering practical questions concerning information transfer. An information network includes: (1) users, (2) information resources, (3) information centers, and (4) the total information transfer structure linking (1), (2), and…

  3. Improving access to health information for older migrants by using grounded theory and social network analysis to understand their information behaviour and digital technology use.

    PubMed

    Goodall, K T; Newman, L A; Ward, P R

    2014-11-01

    Migrant well-being can be strongly influenced by the migration experience and subsequent degree of mainstream language acquisition. There is little research on how older Culturally And Linguistically Diverse (CALD) migrants who have 'aged in place' find health information, and the role which digital technology plays in this. Although the research for this paper was not focused on cancer, we draw out implications for providing cancer-related information to this group. We interviewed 54 participants (14 men and 40 women) aged 63-94 years, who were born in Italy or Greece, and who migrated to Australia mostly as young adults after World War II. Constructivist grounded theory and social network analysis were used for data analysis. Participants identified doctors, adult children, local television, spouse, local newspaper and radio as the most important information sources. They did not generally use computers, the Internet or mobile phones to access information. Literacy in their birth language, and the degree of proficiency in understanding and using English, influenced the range of information sources accessed and the means used. The ways in which older CALD migrants seek and access information has important implications for how professionals and policymakers deliver relevant information to them about cancer prevention, screening, support and treatment, particularly as information and resources are moved online as part of e-health.

  4. Improving access to health information for older migrants by using grounded theory and social network analysis to understand their information behaviour and digital technology use.

    PubMed

    Goodall, K T; Newman, L A; Ward, P R

    2014-11-01

    Migrant well-being can be strongly influenced by the migration experience and subsequent degree of mainstream language acquisition. There is little research on how older Culturally And Linguistically Diverse (CALD) migrants who have 'aged in place' find health information, and the role which digital technology plays in this. Although the research for this paper was not focused on cancer, we draw out implications for providing cancer-related information to this group. We interviewed 54 participants (14 men and 40 women) aged 63-94 years, who were born in Italy or Greece, and who migrated to Australia mostly as young adults after World War II. Constructivist grounded theory and social network analysis were used for data analysis. Participants identified doctors, adult children, local television, spouse, local newspaper and radio as the most important information sources. They did not generally use computers, the Internet or mobile phones to access information. Literacy in their birth language, and the degree of proficiency in understanding and using English, influenced the range of information sources accessed and the means used. The ways in which older CALD migrants seek and access information has important implications for how professionals and policymakers deliver relevant information to them about cancer prevention, screening, support and treatment, particularly as information and resources are moved online as part of e-health. PMID:25250535

  5. Physics as Information Theory

    SciTech Connect

    D'Ariano, Giacomo Mauro

    2010-10-20

    The experience from Quantum Information of the last twenty years has lead theorists to look at Quantum Theory and the whole of Physics from a different angle. A new information-theoretic paradigm is emerging, long time ago prophesied by John Archibald Wheeler with his popular coinage 'It from bit'. Theoretical groups are now addressing the problem of deriving Quantum Theory from informational principles, and similar lines are investigated in new approaches to Quantum Gravity. In my talk I will review some recent advances on these lines. The general idea synthesizing the new paradigm is that there is only Quantum Theory (without quantization rules): the whole Physics--including space-time and relativity--is emergent from quantum-information processing. And, since Quantum Theory itself is made with purely informational principles, the whole Physics must be reformulated in information-theoretical terms. The review is divided into the following parts: (a) The informational axiomatization of Quantum Theory; (b) How space-time and relativistic covariance emerge from the quantum computation; (c) What is the information-theoretical meaning of inertial mass and Planck constant, and how the quantum field emerges; (d) Observational consequences: mass-dependent refraction index of vacuum. I then conclude with some possible future research lines.

  6. Constructor theory of information

    PubMed Central

    Deutsch, David; Marletto, Chiara

    2015-01-01

    We propose a theory of information expressed solely in terms of which transformations of physical systems are possible and which are impossible—i.e. in constructor-theoretic terms. It includes conjectured, exact laws of physics expressing the regularities that allow information to be physically instantiated. Although these laws are directly about information, independently of the details of particular physical instantiations, information is not regarded as an a priori mathematical or logical concept, but as something whose nature and properties are determined by the laws of physics alone. This theory solves a problem at the foundations of existing information theory, namely that information and distinguishability are each defined in terms of the other. It also explains the relationship between classical and quantum information, and reveals the single, constructor-theoretic property underlying the most distinctive phenomena associated with the latter, including the lack of in-principle distinguishability of some states, the impossibility of cloning, the existence of pairs of variables that cannot simultaneously have sharp values, the fact that measurement processes can be both deterministic and unpredictable, the irreducible perturbation caused by measurement, and locally inaccessible information (as in entangled systems). PMID:25663803

  7. The theory of macromolecular networks.

    PubMed

    Edwards, S F

    1986-01-01

    Network theory has evolved from the simplest rubber materials and is just entering the biological area. The existing theories are studied and the modifications required for biological applications are reviewed. The issues involved are: the balance between entropy and internal energy, the rigidity and flexibility of molecules, the stability of the networks, the origin of the intermolecular forces and the entanglement problems of swelling and precipitation.

  8. Information Networks in Biomedicine

    ERIC Educational Resources Information Center

    Millard, William L.

    1975-01-01

    Describes current biomedical information networks, focusing on those with an educational function, and elaborates on the problems encountered in planning, implementing, utilizing and evaluating such networks. Journal of Biocommunication, T. Banks, Educ. TV-431N, U. of Calif., San Francisco 94143. Subscription Rates: individuals and libraries,…

  9. Network Security Validation Using Game Theory

    NASA Astrophysics Data System (ADS)

    Papadopoulou, Vicky; Gregoriades, Andreas

    Non-functional requirements (NFR) such as network security recently gained widespread attention in distributed information systems. Despite their importance however, there is no systematic approach to validate these requirements given the complexity and uncertainty characterizing modern networks. Traditionally, network security requirements specification has been the results of a reactive process. This however, limited the immunity property of the distributed systems that depended on these networks. Security requirements specification need a proactive approach. Networks' infrastructure is constantly under attack by hackers and malicious software that aim to break into computers. To combat these threats, network designers need sophisticated security validation techniques that will guarantee the minimum level of security for their future networks. This paper presents a game-theoretic approach to security requirements validation. An introduction to game theory is presented along with an example that demonstrates the application of the approach.

  10. Classical Information Theory

    NASA Astrophysics Data System (ADS)

    Suhov, Y.

    We begin with the definition of information gained by knowing that an event A has occurred: iota (A) = -log_2 {{P}}(A). (A dual point of view is also useful (although more evasive), where iota (A) is the amount of information needed to specify event A.) Here and below {{P}} stands for the underlying probability distribution. So the rarer an event A, the more information we gain if we know it has occurred. (More broadly, the rarer an event A, the more impact it will have. For example, the unlikely event that occurred in 1938 when fishermen caught a coelacanth - a prehistoric fish believed to be extinct - required a significant change to beliefs about evolution and biology. On the other hand, the likely event of catching a herring or a tuna would hardly imply any change in theories.)

  11. Psychology and social networks: a dynamic network theory perspective.

    PubMed

    Westaby, James D; Pfaff, Danielle L; Redding, Nicholas

    2014-04-01

    Research on social networks has grown exponentially in recent years. However, despite its relevance, the field of psychology has been relatively slow to explain the underlying goal pursuit and resistance processes influencing social networks in the first place. In this vein, this article aims to demonstrate how a dynamic network theory perspective explains the way in which social networks influence these processes and related outcomes, such as goal achievement, performance, learning, and emotional contagion at the interpersonal level of analysis. The theory integrates goal pursuit, motivation, and conflict conceptualizations from psychology with social network concepts from sociology and organizational science to provide a taxonomy of social network role behaviors, such as goal striving, system supporting, goal preventing, system negating, and observing. This theoretical perspective provides psychologists with new tools to map social networks (e.g., dynamic network charts), which can help inform the development of change interventions. Implications for social, industrial-organizational, and counseling psychology as well as conflict resolution are discussed, and new opportunities for research are highlighted, such as those related to dynamic network intelligence (also known as cognitive accuracy), levels of analysis, methodological/ethical issues, and the need to theoretically broaden the study of social networking and social media behavior. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

  12. An information theory account of cognitive control

    PubMed Central

    Fan, Jin

    2014-01-01

    Our ability to efficiently process information and generate appropriate responses depends on the processes collectively called cognitive control. Despite a considerable focus in the literature on the cognitive control of information processing, neural mechanisms underlying control are still unclear, and have not been characterized by considering the quantity of information to be processed. A novel and comprehensive account of cognitive control is proposed using concepts from information theory, which is concerned with communication system analysis and the quantification of information. This account treats the brain as an information-processing entity where cognitive control and its underlying brain networks play a pivotal role in dealing with conditions of uncertainty. This hypothesis and theory article justifies the validity and properties of such an account and relates experimental findings to the frontoparietal network under the framework of information theory. PMID:25228875

  13. An information theory account of cognitive control.

    PubMed

    Fan, Jin

    2014-01-01

    Our ability to efficiently process information and generate appropriate responses depends on the processes collectively called cognitive control. Despite a considerable focus in the literature on the cognitive control of information processing, neural mechanisms underlying control are still unclear, and have not been characterized by considering the quantity of information to be processed. A novel and comprehensive account of cognitive control is proposed using concepts from information theory, which is concerned with communication system analysis and the quantification of information. This account treats the brain as an information-processing entity where cognitive control and its underlying brain networks play a pivotal role in dealing with conditions of uncertainty. This hypothesis and theory article justifies the validity and properties of such an account and relates experimental findings to the frontoparietal network under the framework of information theory.

  14. Information Theory in Analytical Chemistry.

    ERIC Educational Resources Information Center

    Eckschlager, Karel; Stepanek, Vladimir

    1982-01-01

    Discusses information theory in analytical practice. Topics include information quantities; ways of obtaining formulas for the amount of information in structural, qualitative, and trace analyses; and information measures in comparing and optimizing analytical methods and procedures. Includes tables outlining applications of information theory to…

  15. Translating the Interconnections between Ecological and Hydrological Processes in a Small Watershed into Process Networks using Information Theory

    NASA Astrophysics Data System (ADS)

    Kim, J.; Woo, N. C.; Kim, S.; Yun, J.; Kim, S.; Kang, M.; Cho, C. H.; Chun, J. H.

    2014-12-01

    We demonstrate how field measurements can inform the selection of model frameworks in small watershed applications. Based on the assumption that ecohydrological systems are open and complex, we employ the process network analysis to identify the system state and the subsystems architecture with changing environment conditions. Ecohydrological and biogeochemical processes in a watershed can be viewed as a network of processes of a wide range of scales involving various feedback loops and time delay. Using the KoFlux tower-based measurements of energy, water and CO2 flux time series along with those representing the soil-plant-atmospheric continuum; we evaluated statistical measures of characterizing the organization of the information flows in the system. We used Shannon's information entropy and calculated the mutual information and transfer entropy, following Ruddell and Kumar (2009). Transfer entropy can measure the relative strength and time scale of couplings between the variables. In this analysis, we selected 15 variables associated with ecohydrological processes, which are groundwater table height, water temperature, specific conductivity, soil moisture contents at three depths, ecosystem respiration, gross primary productivity, sensible heat flux, latent heat flux, precipitation, air temperature, vapor pressure deficit, atmospheric pressure, and solar radiation. The data-driven nature of this investigation may shed a light on reconciling model parsimony with equifinality in small watershed applications. (Acknowledgment: This work and the data used in the study were funded by the Korea Meteorological Administration Research and Development Program under Grant Weather Information Service Engine (WISE) project,153-3100-3133-302-350 and Grant CATER 2014-3030, respectively. The KoFlux site was supported by the Long-term Ecological Study and Monitoring of Forest Ecosystem Project of Korea Forest Research Institute.)

  16. Reassembling the Information Technology Innovation Process: An Actor Network Theory Method for Managing the Initiation, Production, and Diffusion of Innovations

    NASA Astrophysics Data System (ADS)

    Zendejas, Gerardo; Chiasson, Mike

    This paper will propose and explore a method to enhance focal actors' abilities to enroll and control the many social and technical components interacting during the initiation, production, and diffusion of innovations. The reassembling and stabilizing of such components is the challenging goal of the focal actors involved in these processes. To address this possibility, a healthcare project involving the initiation, production, and diffusion of an IT-based innovation will be influenced by the researcher, using concepts from actor network theory (ANT), within an action research methodology (ARM). The experiences using this method, and the nature of enrolment and translation during its use, will highlight if and how ANT can provide a problem-solving method to help assemble the social and technical actants involved in the diffusion of an innovation. Finally, the paper will discuss the challenges and benefits of implementing such methods to attain widespread diffusion.

  17. Potential Theory for Directed Networks

    PubMed Central

    Zhang, Qian-Ming; Lü, Linyuan; Wang, Wen-Qiang; Zhou, Tao

    2013-01-01

    Uncovering factors underlying the network formation is a long-standing challenge for data mining and network analysis. In particular, the microscopic organizing principles of directed networks are less understood than those of undirected networks. This article proposes a hypothesis named potential theory, which assumes that every directed link corresponds to a decrease of a unit potential and subgraphs with definable potential values for all nodes are preferred. Combining the potential theory with the clustering and homophily mechanisms, it is deduced that the Bi-fan structure consisting of 4 nodes and 4 directed links is the most favored local structure in directed networks. Our hypothesis receives strongly positive supports from extensive experiments on 15 directed networks drawn from disparate fields, as indicated by the most accurate and robust performance of Bi-fan predictor within the link prediction framework. In summary, our main contribution is twofold: (i) We propose a new mechanism for the local organization of directed networks; (ii) We design the corresponding link prediction algorithm, which can not only testify our hypothesis, but also find out direct applications in missing link prediction and friendship recommendation. PMID:23408979

  18. Congenital Heart Information Network

    MedlinePlus

    ... Barmash and Uwe Baemayr for The Congenital Heart Information Network Exempt organization under Section 501(c)3. Copyright ©1996 - 2016 C.H.I.N. All rights reserved TX4-390-685 Original site design and HTML by Panoptic Communications

  19. Information cascade on networks

    NASA Astrophysics Data System (ADS)

    Hisakado, Masato; Mori, Shintaro

    2016-05-01

    In this paper, we discuss a voting model by considering three different kinds of networks: a random graph, the Barabási-Albert (BA) model, and a fitness model. A voting model represents the way in which public perceptions are conveyed to voters. Our voting model is constructed by using two types of voters-herders and independents-and two candidates. Independents conduct voting based on their fundamental values; on the other hand, herders base their voting on the number of previous votes. Hence, herders vote for the majority candidates and obtain information relating to previous votes from their networks. We discuss the difference between the phases on which the networks depend. Two kinds of phase transitions, an information cascade transition and a super-normal transition, were identified. The first of these is a transition between a state in which most voters make the correct choices and a state in which most of them are wrong. The second is a transition of convergence speed. The information cascade transition prevails when herder effects are stronger than the super-normal transition. In the BA and fitness models, the critical point of the information cascade transition is the same as that of the random network model. However, the critical point of the super-normal transition disappears when these two models are used. In conclusion, the influence of networks is shown to only affect the convergence speed and not the information cascade transition. We are therefore able to conclude that the influence of hubs on voters' perceptions is limited.

  20. Quantum Theory is an Information Theory

    NASA Astrophysics Data System (ADS)

    D'Ariano, Giacomo M.; Perinotti, Paolo

    2016-03-01

    In this paper we review the general framework of operational probabilistic theories (OPT), along with the six axioms from which quantum theory can be derived. We argue that the OPT framework along with a relaxed version of five of the axioms, define a general information theory. We close the paper with considerations about the role of the observer in an OPT, and the interpretation of the von Neumann postulate and the Schrödinger-cat paradox.

  1. Computer and information networks.

    PubMed

    Greenberger, M; Aronofsky, J; McKenney, J L; Massy, W F

    1973-10-01

    The most basic conclusion coming out of the EDUCOM seminars is that computer networking must be acknowledged as an important new mode for obtaining information and computation (15). It is a real alternative that needs to be given serious attention in current planning and decision-making. Yet the fact is that many institutions are not taking account of networks when they confer on whether or how to replace their main computer. Articulation of the possibilities of computer networks goes back to the early 1960's and before, and working networks have been in evidence for several years now, both commercially and in universities. What is new, however, is the unmistakable recognition-bordering on a sense of the inevitable-that networks are finally practical and here to stay. The visionary and promotional phases of computer networks are over. It is time for hard-nosed comparative analysis (16). Another conclusion of the seminars has to do with the factors that hinder the fuller development of networking. The major problems to be overcome in applying networks to research and education are political, organizational, and economic in nature rather than technological. This is not to say that the hardware and software problems of linking computers and information systems are completely solved, but they are not the big bottlenecks at present. Research and educational institutions must find ways to organize themselves as well as their computers to work together for greater resource sharing. The coming of age of networks takes on special significance as a result of widespread dissatisfactions expressed with the present computing situation. There is a feeling that the current mode of autonomous, self-sufficient operation in the provision of computing and information services is frequently wasteful, deficient, and unresponsive to users' needs because of duplication of effort from one installation to another, incompatibilities, and inadequate documentation, program support, and user

  2. Computer and information networks.

    PubMed

    Greenberger, M; Aronofsky, J; McKenney, J L; Massy, W F

    1973-10-01

    The most basic conclusion coming out of the EDUCOM seminars is that computer networking must be acknowledged as an important new mode for obtaining information and computation (15). It is a real alternative that needs to be given serious attention in current planning and decision-making. Yet the fact is that many institutions are not taking account of networks when they confer on whether or how to replace their main computer. Articulation of the possibilities of computer networks goes back to the early 1960's and before, and working networks have been in evidence for several years now, both commercially and in universities. What is new, however, is the unmistakable recognition-bordering on a sense of the inevitable-that networks are finally practical and here to stay. The visionary and promotional phases of computer networks are over. It is time for hard-nosed comparative analysis (16). Another conclusion of the seminars has to do with the factors that hinder the fuller development of networking. The major problems to be overcome in applying networks to research and education are political, organizational, and economic in nature rather than technological. This is not to say that the hardware and software problems of linking computers and information systems are completely solved, but they are not the big bottlenecks at present. Research and educational institutions must find ways to organize themselves as well as their computers to work together for greater resource sharing. The coming of age of networks takes on special significance as a result of widespread dissatisfactions expressed with the present computing situation. There is a feeling that the current mode of autonomous, self-sufficient operation in the provision of computing and information services is frequently wasteful, deficient, and unresponsive to users' needs because of duplication of effort from one installation to another, incompatibilities, and inadequate documentation, program support, and user

  3. Informational derivation of quantum theory

    NASA Astrophysics Data System (ADS)

    Chiribella, Giulio; D'Ariano, Giacomo Mauro; Perinotti, Paolo

    2011-07-01

    We derive quantum theory from purely informational principles. Five elementary axioms—causality, perfect distinguishability, ideal compression, local distinguishability, and pure conditioning—define a broad class of theories of information processing that can be regarded as standard. One postulate—purification—singles out quantum theory within this class.

  4. Informational derivation of quantum theory

    SciTech Connect

    Chiribella, Giulio; D'Ariano, Giacomo Mauro; Perinotti, Paolo

    2011-07-15

    We derive quantum theory from purely informational principles. Five elementary axioms - causality, perfect distinguishability, ideal compression, local distinguishability, and pure conditioning - define a broad class of theories of information processing that can be regarded as standard. One postulate - purification - singles out quantum theory within this class.

  5. On directed information theory and Granger causality graphs.

    PubMed

    Amblard, Pierre-Olivier; Michel, Olivier J J

    2011-02-01

    Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems.

  6. Information recovery in behavioral networks.

    PubMed

    Squartini, Tiziano; Ser-Giacomi, Enrico; Garlaschelli, Diego; Judge, George

    2015-01-01

    In the context of agent based modeling and network theory, we focus on the problem of recovering behavior-related choice information from origin-destination type data, a topic also known under the name of network tomography. As a basis for predicting agents' choices we emphasize the connection between adaptive intelligent behavior, causal entropy maximization, and self-organized behavior in an open dynamic system. We cast this problem in the form of binary and weighted networks and suggest information theoretic entropy-driven methods to recover estimates of the unknown behavioral flow parameters. Our objective is to recover the unknown behavioral values across the ensemble analytically, without explicitly sampling the configuration space. In order to do so, we consider the Cressie-Read family of entropic functionals, enlarging the set of estimators commonly employed to make optimal use of the available information. More specifically, we explicitly work out two cases of particular interest: Shannon functional and the likelihood functional. We then employ them for the analysis of both univariate and bivariate data sets, comparing their accuracy in reproducing the observed trends.

  7. Network Information System

    1996-05-01

    The Network Information System (NWIS) was initially implemented in May 1996 as a system in which computing devices could be recorded so that unique names could be generated for each device. Since then the system has grown to be an enterprise wide information system which is integrated with other systems to provide the seamless flow of data through the enterprise. The system Iracks data for two main entities: people and computing devices. The following aremore » the type of functions performed by NWIS for these two entities: People Provides source information to the enterprise person data repository for select contractors and visitors Generates and tracks unique usernames and Unix user IDs for every individual granted cyber access Tracks accounts for centrally managed computing resources, and monitors and controls the reauthorization of the accounts in accordance with the DOE mandated interval Computing Devices Generates unique names for all computing devices registered in the system Tracks the following information for each computing device: manufacturer, make, model, Sandia property number, vendor serial number, operating system and operating system version, owner, device location, amount of memory, amount of disk space, and level of support provided for the machine Tracks the hardware address for network cards Tracks the P address registered to computing devices along with the canonical and alias names for each address Updates the Dynamic Domain Name Service (DDNS) for canonical and alias names Creates the configuration files for DHCP to control the DHCP ranges and allow access to only properly registered computers Tracks and monitors classified security plans for stand-alone computers Tracks the configuration requirements used to setup the machine Tracks the roles people have on machines (system administrator, administrative access, user, etc...) Allows systems administrators to track changes made on the machine (both hardware and software) Generates an

  8. Informed Grounded Theory

    ERIC Educational Resources Information Center

    Thornberg, Robert

    2012-01-01

    There is a widespread idea that in grounded theory (GT) research, the researcher has to delay the literature review until the end of the analysis to avoid contamination--a dictum that might turn educational researchers away from GT. Nevertheless, in this article the author (a) problematizes the dictum of delaying a literature review in classic…

  9. Ohio: Library and Information Networks.

    ERIC Educational Resources Information Center

    Byerly, Greg

    1996-01-01

    Describes the development, current status, and future goals and plans of library and information networks in Ohio. Highlights include OCLC; OhioLINK, incorporating state-assisted universities, two-year colleges, the state library, and private university libraries; a school library network; a public library information network; a telecommunications…

  10. Extracting information from multiplex networks.

    PubMed

    Iacovacci, Jacopo; Bianconi, Ginestra

    2016-06-01

    Multiplex networks are generalized network structures that are able to describe networks in which the same set of nodes are connected by links that have different connotations. Multiplex networks are ubiquitous since they describe social, financial, engineering, and biological networks as well. Extending our ability to analyze complex networks to multiplex network structures increases greatly the level of information that is possible to extract from big data. For these reasons, characterizing the centrality of nodes in multiplex networks and finding new ways to solve challenging inference problems defined on multiplex networks are fundamental questions of network science. In this paper, we discuss the relevance of the Multiplex PageRank algorithm for measuring the centrality of nodes in multilayer networks and we characterize the utility of the recently introduced indicator function Θ̃(S) for describing their mesoscale organization and community structure. As working examples for studying these measures, we consider three multiplex network datasets coming for social science. PMID:27368796

  11. Extracting information from multiplex networks.

    PubMed

    Iacovacci, Jacopo; Bianconi, Ginestra

    2016-06-01

    Multiplex networks are generalized network structures that are able to describe networks in which the same set of nodes are connected by links that have different connotations. Multiplex networks are ubiquitous since they describe social, financial, engineering, and biological networks as well. Extending our ability to analyze complex networks to multiplex network structures increases greatly the level of information that is possible to extract from big data. For these reasons, characterizing the centrality of nodes in multiplex networks and finding new ways to solve challenging inference problems defined on multiplex networks are fundamental questions of network science. In this paper, we discuss the relevance of the Multiplex PageRank algorithm for measuring the centrality of nodes in multilayer networks and we characterize the utility of the recently introduced indicator function Θ̃(S) for describing their mesoscale organization and community structure. As working examples for studying these measures, we consider three multiplex network datasets coming for social science.

  12. Extracting information from multiplex networks

    NASA Astrophysics Data System (ADS)

    Iacovacci, Jacopo; Bianconi, Ginestra

    2016-06-01

    Multiplex networks are generalized network structures that are able to describe networks in which the same set of nodes are connected by links that have different connotations. Multiplex networks are ubiquitous since they describe social, financial, engineering, and biological networks as well. Extending our ability to analyze complex networks to multiplex network structures increases greatly the level of information that is possible to extract from big data. For these reasons, characterizing the centrality of nodes in multiplex networks and finding new ways to solve challenging inference problems defined on multiplex networks are fundamental questions of network science. In this paper, we discuss the relevance of the Multiplex PageRank algorithm for measuring the centrality of nodes in multilayer networks and we characterize the utility of the recently introduced indicator function Θ ˜ S for describing their mesoscale organization and community structure. As working examples for studying these measures, we consider three multiplex network datasets coming for social science.

  13. Information: theory, brain, and behavior.

    PubMed

    Jensen, Greg; Ward, Ryan D; Balsam, Peter D

    2013-11-01

    In the 65 years since its formal specification, information theory has become an established statistical paradigm, providing powerful tools for quantifying probabilistic relationships. Behavior analysis has begun to adopt these tools as a novel means of measuring the interrelations between behavior, stimuli, and contingent outcomes. This approach holds great promise for making more precise determinations about the causes of behavior and the forms in which conditioning may be encoded by organisms. In addition to providing an introduction to the basics of information theory, we review some of the ways that information theory has informed the studies of Pavlovian conditioning, operant conditioning, and behavioral neuroscience. In addition to enriching each of these empirical domains, information theory has the potential to act as a common statistical framework by which results from different domains may be integrated, compared, and ultimately unified. PMID:24122456

  14. Information: theory, brain, and behavior.

    PubMed

    Jensen, Greg; Ward, Ryan D; Balsam, Peter D

    2013-11-01

    In the 65 years since its formal specification, information theory has become an established statistical paradigm, providing powerful tools for quantifying probabilistic relationships. Behavior analysis has begun to adopt these tools as a novel means of measuring the interrelations between behavior, stimuli, and contingent outcomes. This approach holds great promise for making more precise determinations about the causes of behavior and the forms in which conditioning may be encoded by organisms. In addition to providing an introduction to the basics of information theory, we review some of the ways that information theory has informed the studies of Pavlovian conditioning, operant conditioning, and behavioral neuroscience. In addition to enriching each of these empirical domains, information theory has the potential to act as a common statistical framework by which results from different domains may be integrated, compared, and ultimately unified.

  15. Publications of the Jet Propulsion Laboratory, January through December 1974. [deep space network, Apollo project, information theory, and space exploration

    NASA Technical Reports Server (NTRS)

    1975-01-01

    Formalized technical reporting is described and indexed, which resulted from scientific and engineering work performed, or managed, by the Jet Propulsion Laboratory. The five classes of publications included are technical reports, technical memorandums, articles from the bimonthly Deep Space Network Progress Report, special publications, and articles published in the open literature. The publications are indexed by author, subject, and publication type and number.

  16. Information Networking in Population Education.

    ERIC Educational Resources Information Center

    United Nations Educational, Scientific, and Cultural Organization, Bangkok (Thailand). Regional Office for Education in Asia and the Pacific.

    The rapidly increasing body of knowledge in population education has created the need for systematic and effective information services. Information networking entails sharing resources so that the information needs of all network participants are met. The goals of this manual are to: (1) instill in population education specialists a more…

  17. Advances in the Theory of Complex Networks

    NASA Astrophysics Data System (ADS)

    Peruani, Fernando

    An exhaustive and comprehensive review on the theory of complex networks would imply nowadays a titanic task, and it would result in a lengthy work containing plenty of technical details of arguable relevance. Instead, this chapter addresses very briefly the ABC of complex network theory, visiting only the hallmarks of the theoretical founding, to finally focus on two of the most interesting and promising current research problems: the study of dynamical processes on transportation networks and the identification of communities in complex networks.

  18. Complex Networks: from Graph Theory to Biology

    NASA Astrophysics Data System (ADS)

    Lesne, Annick

    2006-12-01

    The aim of this text is to show the central role played by networks in complex system science. A remarkable feature of network studies is to lie at the crossroads of different disciplines, from mathematics (graph theory, combinatorics, probability theory) to physics (statistical physics of networks) to computer science (network generating algorithms, combinatorial optimization) to biological issues (regulatory networks). New paradigms recently appeared, like that of ‘scale-free networks’ providing an alternative to the random graph model introduced long ago by Erdös and Renyi. With the notion of statistical ensemble and methods originally introduced for percolation networks, statistical physics is of high relevance to get a deep account of topological and statistical properties of a network. Then their consequences on the dynamics taking place in the network should be investigated. Impact of network theory is huge in all natural sciences, especially in biology with gene networks, metabolic networks, neural networks or food webs. I illustrate this brief overview with a recent work on the influence of network topology on the dynamics of coupled excitable units, and the insights it provides about network emerging features, robustness of network behaviors, and the notion of static or dynamic motif.

  19. Social Network Theory and Educational Change

    ERIC Educational Resources Information Center

    Daly, Alan J., Ed.

    2010-01-01

    "Social Network Theory and Educational Change" offers a provocative and fascinating exploration of how social networks in schools can impede or facilitate the work of education reform. Drawing on the work of leading scholars, the book comprises a series of studies examining networks among teachers and school leaders, contrasting formal and…

  20. Information theory in molecular biology

    NASA Astrophysics Data System (ADS)

    Adami, Christoph

    2004-04-01

    This article introduces the physics of information in the context of molecular biology and genomics. Entropy and information, the two central concepts of Shannon's theory of information and communication, are often confused with each other but play transparent roles when applied to statistical ensembles (i.e., identically prepared sets) of symbolic sequences. Such an approach can distinguish between entropy and information in genes, predict the secondary structure of ribozymes, and detect the covariation between residues in folded proteins. We also review applications to molecular sequence and structure analysis, and introduce new tools in the characterization of resistance mutations, and in drug design.

  1. Beyond mean field theory: statistical field theory for neural networks

    PubMed Central

    Buice, Michael A; Chow, Carson C

    2014-01-01

    Mean field theories have been a stalwart for studying the dynamics of networks of coupled neurons. They are convenient because they are relatively simple and possible to analyze. However, classical mean field theory neglects the effects of fluctuations and correlations due to single neuron effects. Here, we consider various possible approaches for going beyond mean field theory and incorporating correlation effects. Statistical field theory methods, in particular the Doi–Peliti–Janssen formalism, are particularly useful in this regard. PMID:25243014

  2. Epistasis analysis using information theory.

    PubMed

    Moore, Jason H; Hu, Ting

    2015-01-01

    Here we introduce entropy-based measures derived from information theory for detecting and characterizing epistasis in genetic association studies. We provide a general overview of the methods and highlight some of the modifications that have greatly improved its power for genetic analysis. We end with a few published studies of complex human diseases that have used these measures.

  3. Building a Unified Information Network.

    ERIC Educational Resources Information Center

    Avram, Henriette D.

    1988-01-01

    Discusses cooperative efforts between research organizations and libraries to create a national information network. Topics discussed include the Linked System Project (LSP); technical processing versus reference and research functions; Open Systems Interconnection (OSI) Reference Model; the National Science Foundation Network (NSFNET); and…

  4. Analysis of surface-water data network in Kansas for effectiveness in providing regional streamflow information; with a section on theory and application of generalized least squares

    USGS Publications Warehouse

    Medina, K.D.; Tasker, Gary D.

    1987-01-01

    This report documents the results of an analysis of the surface-water data network in Kansas for its effectiveness in providing regional streamflow information. The network was analyzed using generalized least squares regression. The correlation and time-sampling error of the streamflow characteristic are considered in the generalized least squares method. Unregulated medium-, low-, and high-flow characteristics were selected to be representative of the regional information that can be obtained from streamflow-gaging-station records for use in evaluating the effectiveness of continuing the present network stations, discontinuing some stations, and (or) adding new stations. The analysis used streamflow records for all currently operated stations that were not affected by regulation and for discontinued stations for which unregulated flow characteristics, as well as physical and climatic characteristics, were available. The State was divided into three network areas, western, northeastern, and southeastern Kansas, and analysis was made for the three streamflow characteristics in each area, using three planning horizons. The analysis showed that the maximum reduction of sampling mean-square error for each cost level could be obtained by adding new stations and discontinuing some current network stations. Large reductions in sampling mean-square error for low-flow information could be achieved in all three network areas, the reduction in western Kansas being the most dramatic. The addition of new stations would be most beneficial for mean-flow information in western Kansas. The reduction of sampling mean-square error for high-flow information would benefit most from the addition of new stations in western Kansas. Southeastern Kansas showed the smallest error reduction in high-flow information. A comparison among all three network areas indicated that funding resources could be most effectively used by discontinuing more stations in northeastern and southeastern Kansas

  5. Networking Theories by Iterative Unpacking

    ERIC Educational Resources Information Center

    Koichu, Boris

    2014-01-01

    An iterative unpacking strategy consists of sequencing empirically-based theoretical developments so that at each step of theorizing one theory serves as an overarching conceptual framework, in which another theory, either existing or emerging, is embedded in order to elaborate on the chosen element(s) of the overarching theory. The strategy is…

  6. Network Information Management Subsystem

    NASA Technical Reports Server (NTRS)

    Chatburn, C. C.

    1985-01-01

    The Deep Space Network is implementing a distributed data base management system in which the data are shared among several applications and the host machines are not totally dedicated to a particular application. Since the data and resources are to be shared, the equipment must be operated carefully so that the resources are shared equitably. The current status of the project is discussed and policies, roles, and guidelines are recommended for the organizations involved in the project.

  7. Application of Information Integration Theory to Methodology of Theory Development.

    ERIC Educational Resources Information Center

    Shanteau, James

    Information integration theory (IIT) seeks to develop a unified theory of judgment and behavior. This theory provides a conceptual framework that has been applied to a variety of research areas including personality impression formation and decision making. In these applications information integration theory has helped to resolve methodological…

  8. Ranking Information in Networks

    NASA Astrophysics Data System (ADS)

    Eliassi-Rad, Tina; Henderson, Keith

    Given a network, we are interested in ranking sets of nodes that score highest on user-specified criteria. For instance in graphs from bibliographic data (e.g. PubMed), we would like to discover sets of authors with expertise in a wide range of disciplines. We present this ranking task as a Top-K problem; utilize fixed-memory heuristic search; and present performance of both the serial and distributed search algorithms on synthetic and real-world data sets.

  9. Workplace Learning in Informal Networks

    ERIC Educational Resources Information Center

    Milligan, Colin; Littlejohn, Allison; Margaryan, Anoush

    2014-01-01

    Learning does not stop when an individual leaves formal education, but becomes increasingly informal, and deeply embedded within other activities such as work. This article describes the challenges of informal learning in knowledge intensive industries, highlighting the important role of personal learning networks. The article argues that…

  10. Information complexity of neural networks.

    PubMed

    Kon, M A; Plaskota, L

    2000-04-01

    This paper studies the question of lower bounds on the number of neurons and examples necessary to program a given task into feed forward neural networks. We introduce the notion of information complexity of a network to complement that of neural complexity. Neural complexity deals with lower bounds for neural resources (numbers of neurons) needed by a network to perform a given task within a given tolerance. Information complexity measures lower bounds for the information (i.e. number of examples) needed about the desired input-output function. We study the interaction of the two complexities, and so lower bounds for the complexity of building and then programming feed-forward nets for given tasks. We show something unexpected a priori--the interaction of the two can be simply bounded, so that they can be studied essentially independently. We construct radial basis function (RBF) algorithms of order n3 that are information-optimal, and give example applications.

  11. Information Assurance in Wireless Networks

    NASA Astrophysics Data System (ADS)

    Kabara, Joseph; Krishnamurthy, Prashant; Tipper, David

    2001-09-01

    Emerging wireless networks will contain a hybrid infrastructure based on fixed, mobile and ad hoc topologies and technologies. In such a dynamic architecture, we define information assurance as the provisions for both information security and information availability. The implications of this definition are that the wireless network architecture must (a) provide sufficient security measures, (b) be survivable under node or link attack or failure and (c) be designed such that sufficient capacity remains for all critical services (and preferably most other services) in the event of attack or component failure. We have begun a research project to investigate the provision of information assurance for wireless networks viz. survivability, security and availability and here discuss the issues and challenges therein.

  12. Information communication on complex networks

    NASA Astrophysics Data System (ADS)

    Igarashi, Akito; Kawamoto, Hiroki; Maruyama, Takahiro; Morioka, Atsushi; Naganuma, Yuki

    2013-02-01

    Since communication networks such as the Internet, which is regarded as a complex network, have recently become a huge scale and a lot of data pass through them, the improvement of packet routing strategies for transport is one of the most significant themes in the study of computer networks. It is especially important to find routing strategies which can bear as many traffic as possible without congestion in complex networks. First, using neural networks, we introduce a strategy for packet routing on complex networks, where path lengths and queue lengths in nodes are taken into account within a framework of statistical physics. Secondly, instead of using shortest paths, we propose efficient paths which avoid hubs, nodes with a great many degrees, on scale-free networks with a weight of each node. We improve the heuristic algorithm proposed by Danila et. al. which optimizes step by step routing properties on congestion by using the information of betweenness, the probability of paths passing through a node in all optimal paths which are defined according to a rule, and mitigates the congestion. We confirm the new heuristic algorithm which balances traffic on networks by achieving minimization of the maximum betweenness in much smaller number of iteration steps. Finally, We model virus spreading and data transfer on peer-to-peer (P2P) networks. Using mean-field approximation, we obtain an analytical formulation and emulate virus spreading on the network and compare the results with those of simulation. Moreover, we investigate the mitigation of information traffic congestion in the P2P networks.

  13. Queuing theory models for computer networks

    NASA Technical Reports Server (NTRS)

    Galant, David C.

    1989-01-01

    A set of simple queuing theory models which can model the average response of a network of computers to a given traffic load has been implemented using a spreadsheet. The impact of variations in traffic patterns and intensities, channel capacities, and message protocols can be assessed using them because of the lack of fine detail in the network traffic rates, traffic patterns, and the hardware used to implement the networks. A sample use of the models applied to a realistic problem is included in appendix A. Appendix B provides a glossary of terms used in this paper. This Ames Research Center computer communication network is an evolving network of local area networks (LANs) connected via gateways and high-speed backbone communication channels. Intelligent planning of expansion and improvement requires understanding the behavior of the individual LANs as well as the collection of networks as a whole.

  14. Recoverability in quantum information theory

    NASA Astrophysics Data System (ADS)

    Wilde, Mark

    The fact that the quantum relative entropy is non-increasing with respect to quantum physical evolutions lies at the core of many optimality theorems in quantum information theory and has applications in other areas of physics. In this work, we establish improvements of this entropy inequality in the form of physically meaningful remainder terms. One of the main results can be summarized informally as follows: if the decrease in quantum relative entropy between two quantum states after a quantum physical evolution is relatively small, then it is possible to perform a recovery operation, such that one can perfectly recover one state while approximately recovering the other. This can be interpreted as quantifying how well one can reverse a quantum physical evolution. Our proof method is elementary, relying on the method of complex interpolation, basic linear algebra, and the recently introduced Renyi generalization of a relative entropy difference. The theorem has a number of applications in quantum information theory, which have to do with providing physically meaningful improvements to many known entropy inequalities. This is based on arXiv:1505.04661, now accepted for publication in Proceedings of the Royal Society A. I acknowledge support from startup funds from the Department of Physics and Astronomy at LSU, the NSF under Award No. CCF-1350397, and the DARPA Quiness Program through US Army Research Office award W31P4Q-12-1-0019.

  15. Information Theory, Inference and Learning Algorithms

    NASA Astrophysics Data System (ADS)

    Mackay, David J. C.

    2003-10-01

    Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

  16. Whether information network supplements friendship network

    NASA Astrophysics Data System (ADS)

    Miao, Lili; Zhang, Qian-Ming; Nie, Da-Cheng; Cai, Shi-Min

    2015-02-01

    Homophily is a significant mechanism for link prediction in complex network, of which principle describes that people with similar profiles or experiences tend to tie with each other. In a multi-relationship network, friendship among people has been utilized to reinforce similarity of taste for recommendation system whose basic idea is similar to homophily, yet how the taste inversely affects friendship prediction is little discussed. This paper contributes to address the issue by analyzing two benchmark data sets both including user's behavioral information of taste and friendship based on the principle of homophily. It can be found that the creation of friendship tightly associates with personal taste. Especially, the behavioral information of taste involving with popular objects is much more effective to improve the performance of friendship prediction. However, this result seems to be contradictory to the finding in Zhang et al. (2013) that the behavior information of taste involving with popular objects is redundant in recommendation system. We thus discuss this inconformity to comprehensively understand the correlation between them.

  17. Modern temporal network theory: a colloquium

    NASA Astrophysics Data System (ADS)

    Holme, Petter

    2015-09-01

    The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it is more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions.

  18. Information transmission in genetic regulatory networks: a review.

    PubMed

    Tkačik, Gašper; Walczak, Aleksandra M

    2011-04-20

    Genetic regulatory networks enable cells to respond to changes in internal and external conditions by dynamically coordinating their gene expression profiles. Our ability to make quantitative measurements in these biochemical circuits has deepened our understanding of what kinds of computations genetic regulatory networks can perform, and with what reliability. These advances have motivated researchers to look for connections between the architecture and function of genetic regulatory networks. Transmitting information between a network's inputs and outputs has been proposed as one such possible measure of function, relevant in certain biological contexts. Here we summarize recent developments in the application of information theory to gene regulatory networks. We first review basic concepts in information theory necessary for understanding recent work. We then discuss the functional complexity of gene regulation, which arises from the molecular nature of the regulatory interactions. We end by reviewing some experiments that support the view that genetic networks responsible for early development of multicellular organisms might be maximizing transmitted 'positional information'.

  19. The application of information theory to biochemical signaling systems.

    PubMed

    Rhee, Alex; Cheong, Raymond; Levchenko, Andre

    2012-08-01

    Cell signaling can be thought of fundamentally as an information transmission problem in which chemical messengers relay information about the external environment to the decision centers within a cell. Due to the biochemical nature of cellular signal transduction networks, molecular noise will inevitably limit the fidelity of any messages received and processed by a cell's signal transduction networks, leaving it with an imperfect impression of its environment. Fortunately, Shannon's information theory provides a mathematical framework independent of network complexity that can quantify the amount of information that can be transmitted despite biochemical noise. In particular, the channel capacity can be used to measure the maximum number of stimuli a cell can distinguish based upon the noisy responses of its signaling systems. Here, we provide a primer for quantitative biologists that covers fundamental concepts of information theory, highlights several key considerations when experimentally measuring channel capacity, and describes successful examples of the application of information theoretic analysis to biological signaling.

  20. Weight-Control Information Network (WIN)

    MedlinePlus

    ... Feature: Reducing Childhood Obesity The Weight-control Information Network (WIN) Past Issues / Spring - Summer 2010 Table of ... here are tips from the Weight-control Information Network (WIN), an information service of the National Institute ...

  1. Towards a predictive theory for genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Tkacik, Gasper

    When cells respond to changes in the environment by regulating the expression levels of their genes, we often draw parallels between these biological processes and engineered information processing systems. One can go beyond this qualitative analogy, however, by analyzing information transmission in biochemical ``hardware'' using Shannon's information theory. Here, gene regulation is viewed as a transmission channel operating under restrictive constraints set by the resource costs and intracellular noise. We present a series of results demonstrating that a theory of information transmission in genetic regulatory circuits feasibly yields non-trivial, testable predictions. These predictions concern strategies by which individual gene regulatory elements, e.g., promoters or enhancers, read out their signals; as well as strategies by which small networks of genes, independently or in spatially coupled settings, respond to their inputs. These predictions can be quantitatively compared to the known regulatory networks and their function, and can elucidate how reproducible biological processes, such as embryonic development, can be orchestrated by networks built out of noisy components. Preliminary successes in the gap gene network of the fruit fly Drosophila indicate that a full ab initio theoretical prediction of a regulatory network is possible, a feat that has not yet been achieved for any real regulatory network. We end by describing open challenges on the path towards such a prediction.

  2. Exploring network theory for mass drug administration.

    PubMed

    Chami, Goylette F; Molyneux, David H; Kontoleon, Andreas A; Dunne, David W

    2013-08-01

    Network theory is a well-established discipline that uses mathematical graphs to describe biological, physical, and social systems. The topologies across empirical networks display strikingly similar organizational properties. In particular, the characteristics of these networks allow computational analysis to contribute data unattainable from examining individual components in isolation. However, the interdisciplinary and quantitative nature of network analysis has yet to be exploited by public health initiatives to distribute preventive chemotherapies. One notable application is the 2012 World Health Organization (WHO) Roadmap for Neglected Tropical Diseases (NTDs) where there is a need to upscale distribution capacity and to target systematic noncompliers. An understanding of local networks for analysing the distributional properties of community-directed treatment may facilitate sustainable expansion of mass drug-administration (MDA) programs.

  3. Research on invulnerability of equipment support information network

    NASA Astrophysics Data System (ADS)

    Sun, Xiao; Liu, Bin; Zhong, Qigen; Cao, Zhiyi

    2013-03-01

    In this paper, the entity composition of equipment support information network is studied, and the network abstract model is built. The influence factors of the invulnerability of equipment support information network are analyzed, and the invulnerability capabilities under random attack are analyzed. According to the centrality theory, the materiality evaluation centralities of the nodes are given, and the invulnerability capabilities under selective attack are analyzed. Finally, the reasons that restrict the invulnerability of equipment support information network are summarized, and the modified principles and methods are given.

  4. Urban traffic-network performance: flow theory and simulation experiments

    SciTech Connect

    Williams, J.C.

    1986-01-01

    Performance models for urban street networks were developed to describe the response of a traffic network to given travel-demand levels. The three basic traffic flow variables, speed, flow, and concentration, are defined at the network level, and three model systems are proposed. Each system consists of a series of interrelated, consistent functions between the three basic traffic-flow variables as well as the fraction of stopped vehicles in the network. These models are subsequently compared with the results of microscopic simulation of a small test network. The sensitivity of one of the model systems to a variety of network features was also explored. Three categories of features were considered, with the specific features tested listed in parentheses: network topology (block length and street width), traffic control (traffic signal coordination), and traffic characteristics (level of inter-vehicular interaction). Finally, a fundamental issue concerning the estimation of two network-level parameters (from a nonlinear relation in the two-fluid theory) was examined. The principal concern was that of comparability of these parameters when estimated with information from a single vehicle (or small group of vehicles), as done in conjunction with previous field studies, and when estimated with network-level information (i.e., all the vehicles), as is possible with simulation.

  5. Polymer networks and gels: Simulation and theory

    NASA Astrophysics Data System (ADS)

    Kenkare, Nirupama Ramamurthy

    1998-12-01

    network pressure is treated as the sum of liquid-like and elastic components. The liquid-like component is obtained by extending the Generalized Flory-Dimer theory to networks, and the elastic component is obtained by treating the network as a set of interpenetrated tree-like structures and using a ideal chain-spring analogy to calculate the free energy. The theoretical predictions for network pressure are in very good agreement with simulation data. Our simulation results for the network chain properties show that the chain end-to-end vectors scale affinely with macroscopic deformation at large densities, but show a weaker-than-affine scaling at low densities. A combined discontinuous molecular dynamics and Monte Carlo simulation technique is used to study the swelling of trifunctional networks of chain lengths 20 and 35 in an athermal solvent. The swelling simulations are conducted under conditions of constant pressure and chemical potential. The gel packing fraction and solvent fraction at swelling equilibrium were found to increase with pressure as expected. We present a simple, analytical theory for gel swelling, grounded in our previous theoretical work for solvent-free networks. The predictions of this theory for the gel properties at swelling equilibrium show remarkably good agreement with simulation results.

  6. Nonequilibrium landscape theory of neural networks.

    PubMed

    Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

    2013-11-01

    The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape-flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments.

  7. Information Security and Privacy in Network Environments.

    ERIC Educational Resources Information Center

    Congress of the U.S., Washington, DC. Office of Technology Assessment.

    The use of information networks for business and government is expanding enormously. Government use of networks features prominently in plans to make government more efficient, effective, and responsive. But the transformation brought about by the networking also raises new concerns for the security and privacy of networked information. This…

  8. Information Theory in Biology after 18 Years

    ERIC Educational Resources Information Center

    Johnson, Horton A.

    1970-01-01

    Reviews applications of information theory to biology, concluding that they have not proved very useful. Suggests modifications and extensions to increase the biological relevance of the theory, and speculates about applications in quantifying cell proliferation, chemical homeostasis and aging. (EB)

  9. Spinal Cord Injury Model System Information Network

    MedlinePlus

    ... the UAB-SCIMS More The UAB-SCIMS Information Network The University of Alabama at Birmingham Spinal Cord Injury Model System (UAB-SCIMS) maintains this Information Network as a resource to promote knowledge in the ...

  10. A novel approach to characterize information radiation in complex networks

    NASA Astrophysics Data System (ADS)

    Wang, Xiaoyang; Wang, Ying; Zhu, Lin; Li, Chao

    2016-06-01

    The traditional research of information dissemination is mostly based on the virus spreading model that the information is being spread by probability, which does not match very well to the reality, because the information that we receive is always more or less than what was sent. In order to quantitatively describe variations in the amount of information during the spreading process, this article proposes a safety information radiation model on the basis of communication theory, combining with relevant theories of complex networks. This model comprehensively considers the various influence factors when safety information radiates in the network, and introduces some concepts from the communication theory perspective, such as the radiation gain function, receiving gain function, information retaining capacity and information second reception capacity, to describe the safety information radiation process between nodes and dynamically investigate the states of network nodes. On a micro level, this article analyzes the influence of various initial conditions and parameters on safety information radiation through the new model simulation. The simulation reveals that this novel approach can reflect the variation of safety information quantity of each node in the complex network, and the scale-free network has better "radiation explosive power", while the small-world network has better "radiation staying power". The results also show that it is efficient to improve the overall performance of network security by selecting nodes with high degrees as the information source, refining and simplifying the information, increasing the information second reception capacity and decreasing the noises. In a word, this article lays the foundation for further research on the interactions of information and energy between internal components within complex systems.

  11. An introductory review of information theory in the context of computational neuroscience.

    PubMed

    McDonnell, Mark D; Ikeda, Shiro; Manton, Jonathan H

    2011-07-01

    This article introduces several fundamental concepts in information theory from the perspective of their origins in engineering. Understanding such concepts is important in neuroscience for two reasons. Simply applying formulae from information theory without understanding the assumptions behind their definitions can lead to erroneous results and conclusions. Furthermore, this century will see a convergence of information theory and neuroscience; information theory will expand its foundations to incorporate more comprehensively biological processes thereby helping reveal how neuronal networks achieve their remarkable information processing abilities.

  12. Biological impacts and context of network theory.

    PubMed

    Almaas, Eivind

    2007-05-01

    Many complex systems can be represented and analyzed as networks, and examples that have benefited from this approach span the natural sciences. For instance, we now know that systems as disparate as the World Wide Web, the Internet, scientific collaborations, food webs, protein interactions and metabolism all have common features in their organization, the most salient of which are their scale-free connectivity distributions and their small-world behavior. The recent availability of large-scale datasets that span the proteome or metabolome of an organism have made it possible to elucidate some of the organizational principles and rules that govern their function, robustness and evolution. We expect that combining the currently separate layers of information from gene regulatory networks, signal transduction networks, protein interaction networks and metabolic networks will dramatically enhance our understanding of cellular function and dynamics.

  13. Post Disaster Governance, Complexity and Network Theory

    PubMed Central

    Lassa, Jonatan A.

    2015-01-01

    This research aims to understand the organizational network typology of large­-scale disaster intervention in developing countries and to understand the complexity of post-­disaster intervention, through the use of network theory based on empirical data from post-­tsunami reconstruction in Aceh, Indonesia, during 2005/­2007. The findings suggest that the ‘ degrees of separation’ (or network diameter) between any two organizations in the field is 5, thus reflecting ‘small­ world’ realities and therefore making no significant difference with the real human networks, as found in previous experiments. There are also significant loops in the network reflecting the fact that some actors tend to not cooperate, which challenges post­ disaster coordination. The findings show the landscape of humanitarian actors is not randomly distributed. Many actors were connected to each other through certain hubs, while hundreds of actors make ‘scattered’ single ‘principal-­client’ links. The paper concludes that by understanding the distribution of degree, centrality, ‘degrees of separation’ and visualization of the network, authorities can improve their understanding of the realities of coordination, from macro to micro scales. PMID:26236562

  14. Information Literacy Instruction: Theory and Practice. Information Literacy Sourcebooks.

    ERIC Educational Resources Information Center

    Grassian, Esther S.; Kaplowitz, Joan R.

    This book discusses the theory and practice of information literacy instruction. Part I provides background, including the definition and history of information literacy instruction. Part II covers information literacy instruction building blocks, including: a brief introduction to learning theory; an overview of learning styles; library anxiety,…

  15. Network theory and its applications in economic systems

    NASA Astrophysics Data System (ADS)

    Huang, Xuqing

    This dissertation covers the two major parts of my Ph.D. research: i) developing theoretical framework of complex networks; and ii) applying complex networks models to quantitatively analyze economics systems. In part I, we focus on developing theories of interdependent networks, which includes two chapters: 1) We develop a mathematical framework to study the percolation of interdependent networks under targeted-attack and find that when the highly connected nodes are protected and have lower probability to fail, in contrast to single scale-free (SF) networks where the percolation threshold pc = 0, coupled SF networks are significantly more vulnerable with pc significantly larger than zero. 2) We analytically demonstrates that clustering, which quantifies the propensity for two neighbors of the same vertex to also be neighbors of each other, significantly increases the vulnerability of the system. In part II, we apply the complex networks models to study economics systems, which also includes two chapters: 1) We study the US corporate governance network, in which nodes representing directors and links between two directors representing their service on common company boards, and propose a quantitative measure of information and influence transformation in the network. Thus we are able to identify the most influential directors in the network. 2) We propose a bipartite networks model to simulate the risk propagation process among commercial banks during financial crisis. With empirical bank's balance sheet data in 2007 as input to the model, we find that our model efficiently identifies a significant portion of the actual failed banks reported by Federal Deposit Insurance Corporation during the financial crisis between 2008 and 2011. The results suggest that complex networks model could be useful for systemic risk stress testing for financial systems. The model also identifies that commercial rather than residential real estate assets are major culprits for the

  16. Describing and Classifying Networked Information Resources.

    ERIC Educational Resources Information Center

    Lynch, Clifford A.; Preston, Cecilia M.

    1992-01-01

    Examines usage scenarios for a database of descriptive information to access networked information resources such as online library catalogs, file archives, online journal article repositories, and information servers. Existing classification schemes and their usefulness for networked information resources are discussed. Specific data elements…

  17. Dissemination Networks: Information Resources for Education.

    ERIC Educational Resources Information Center

    Far West Lab. for Educational Research and Development, San Francisco, CA.

    Descriptive information is provided on 22 networks sponsored by the National Institute of Education (NIE) or the United States Office of Education (USOE) for the dissemination of educational information. The directory is arranged alphabetically by network title, followed by its acronym, sponsoring bureau/office, major functions, network members,…

  18. Local Area Networks for Information Retrieval.

    ERIC Educational Resources Information Center

    Kibirige, Harry M.

    This examination of the use of local area networks (LANs) by libraries summarizes the findings of a nationwide survey of 600 libraries and information centers and 200 microcomputer networking system manufacturers and vendors, which was conducted to determine the relevance of currently available networking systems for library and information center…

  19. Hodge Decomposition of Information Flow on Small-World Networks

    PubMed Central

    Haruna, Taichi; Fujiki, Yuuya

    2016-01-01

    We investigate the influence of the small-world topology on the composition of information flow on networks. By appealing to the combinatorial Hodge theory, we decompose information flow generated by random threshold networks on the Watts-Strogatz model into three components: gradient, harmonic and curl flows. The harmonic and curl flows represent globally circular and locally circular components, respectively. The Watts-Strogatz model bridges the two extreme network topologies, a lattice network and a random network, by a single parameter that is the probability of random rewiring. The small-world topology is realized within a certain range between them. By numerical simulation we found that as networks become more random the ratio of harmonic flow to the total magnitude of information flow increases whereas the ratio of curl flow decreases. Furthermore, both quantities are significantly enhanced from the level when only network structure is considered for the network close to a random network and a lattice network, respectively. Finally, the sum of these two ratios takes its maximum value within the small-world region. These findings suggest that the dynamical information counterpart of global integration and that of local segregation are the harmonic flow and the curl flow, respectively, and that a part of the small-world region is dominated by internal circulation of information flow. PMID:27733817

  20. Dynamic information routing in complex networks

    NASA Astrophysics Data System (ADS)

    Kirst, Christoph; Timme, Marc; Battaglia, Demian

    2016-04-01

    Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this mechanism specifically for oscillatory dynamics and analyse how individual unit properties, the network topology and external inputs co-act to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine nonlocal network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function.

  1. Dynamic information routing in complex networks

    PubMed Central

    Kirst, Christoph; Timme, Marc; Battaglia, Demian

    2016-01-01

    Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this mechanism specifically for oscillatory dynamics and analyse how individual unit properties, the network topology and external inputs co-act to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine nonlocal network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function. PMID:27067257

  2. Reaction networks and evolutionary game theory.

    PubMed

    Veloz, Tomas; Razeto-Barry, Pablo; Dittrich, Peter; Fajardo, Alejandro

    2014-01-01

    The powerful mathematical tools developed for the study of large scale reaction networks have given rise to applications of this framework beyond the scope of biochemistry. Recently, reaction networks have been suggested as an alternative way to model social phenomena. In this "socio-chemical metaphor" molecular species play the role of agents' decisions and their outcomes, and chemical reactions play the role of interactions among these decisions. From here, it is possible to study the dynamical properties of social systems using standard tools of biochemical modelling. In this work we show how to use reaction networks to model systems that are usually studied via evolutionary game theory. We first illustrate our framework by modeling the repeated prisoners' dilemma. The model is built from the payoff matrix together with assumptions of the agents' memory and recognizability capacities. The model provides consistent results concerning the performance of the agents, and allows for the examination of the steady states of the system in a simple manner. We further develop a model considering the interaction among Tit for Tat and Defector agents. We produce analytical results concerning the performance of the strategies in different situations of agents' memory and recognizability. This approach unites two important theories and may produce new insights in classical problems such as the evolution of cooperation in large scale systems.

  3. Pathways, Networks, and Systems: Theory and Experiments

    SciTech Connect

    Joseph H. Nadeau; John D. Lambris

    2004-10-30

    The international conference provided a unique opportunity for theoreticians and experimenters to exchange ideas, strategies, problems, challenges, language and opportunities in both formal and informal settings. This dialog is an important step towards developing a deep and effective integration of theory and experiments in studies of systems biology in humans and model organisms.

  4. Information Processing Theory: Classroom Applications.

    ERIC Educational Resources Information Center

    Slate, John R.; Charlesworth, John R., Jr.

    The information processing model, a theoretical framework of how humans think, reason, and learn, views human cognitive functioning as analogous to the operation of a computer. This paper uses the increased understanding of the information processing model to provide teachers with suggestions for improving the teaching-learning process. Major…

  5. Maximizing information exchange between complex networks

    NASA Astrophysics Data System (ADS)

    West, Bruce J.; Geneston, Elvis L.; Grigolini, Paolo

    2008-10-01

    Science is not merely the smooth progressive interaction of hypothesis, experiment and theory, although it sometimes has that form. More realistically the scientific study of any given complex phenomenon generates a number of explanations, from a variety of perspectives, that eventually requires synthesis to achieve a deep level of insight and understanding. One such synthesis has created the field of out-of-equilibrium statistical physics as applied to the understanding of complex dynamic networks. Over the past forty years the concept of complexity has undergone a metamorphosis. Complexity was originally seen as a consequence of memory in individual particle trajectories, in full agreement with a Hamiltonian picture of microscopic dynamics and, in principle, macroscopic dynamics could be derived from the microscopic Hamiltonian picture. The main difficulty in deriving macroscopic dynamics from microscopic dynamics is the need to take into account the actions of a very large number of components. The existence of events such as abrupt jumps, considered by the conventional continuous time random walk approach to describing complexity was never perceived as conflicting with the Hamiltonian view. Herein we review many of the reasons why this traditional Hamiltonian view of complexity is unsatisfactory. We show that as a result of technological advances, which make the observation of single elementary events possible, the definition of complexity has shifted from the conventional memory concept towards the action of non-Poisson renewal events. We show that the observation of crucial processes, such as the intermittent fluorescence of blinking quantum dots as well as the brain’s response to music, as monitored by a set of electrodes attached to the scalp, has forced investigators to go beyond the traditional concept of complexity and to establish closer contact with the nascent field of complex networks. Complex networks form one of the most challenging areas of

  6. Link Prediction in Complex Networks: A Mutual Information Perspective

    PubMed Central

    Tan, Fei; Xia, Yongxiang; Zhu, Boyao

    2014-01-01

    Topological properties of networks are widely applied to study the link-prediction problem recently. Common Neighbors, for example, is a natural yet efficient framework. Many variants of Common Neighbors have been thus proposed to further boost the discriminative resolution of candidate links. In this paper, we reexamine the role of network topology in predicting missing links from the perspective of information theory, and present a practical approach based on the mutual information of network structures. It not only can improve the prediction accuracy substantially, but also experiences reasonable computing complexity. PMID:25207920

  7. Towards understanding the behavior of physical systems using information theory

    NASA Astrophysics Data System (ADS)

    Quax, Rick; Apolloni, Andrea; Sloot, Peter M. A.

    2013-09-01

    One of the goals of complex network analysis is to identify the most influential nodes, i.e., the nodes that dictate the dynamics of other nodes. In the case of autonomous systems or transportation networks, highly connected hubs play a preeminent role in diffusing the flow of information and viruses; in contrast, in language evolution most linguistic norms come from the peripheral nodes who have only few contacts. Clearly a topological analysis of the interactions alone is not sufficient to identify the nodes that drive the state of the network. Here we show how information theory can be used to quantify how the dynamics of individual nodes propagate through a system. We interpret the state of a node as a storage of information about the state of other nodes, which is quantified in terms of Shannon information. This information is transferred through interactions and lost due to noise, and we calculate how far it can travel through a network. We apply this concept to a model of opinion formation in a complex social network to calculate the impact of each node by measuring how long its opinion is remembered by the network. Counter-intuitively we find that the dynamics of opinions are not determined by the hubs or peripheral nodes, but rather by nodes with an intermediate connectivity.

  8. A security architecture for health information networks.

    PubMed

    Kailar, Rajashekar; Muralidhar, Vinod

    2007-10-11

    Health information network security needs to balance exacting security controls with practicality, and ease of implementation in today's healthcare enterprise. Recent work on 'nationwide health information network' architectures has sought to share highly confidential data over insecure networks such as the Internet. Using basic patterns of health network data flow and trust models to support secure communication between network nodes, we abstract network security requirements to a core set to enable secure inter-network data sharing. We propose a minimum set of security controls that can be implemented without needing major new technologies, but yet realize network security and privacy goals of confidentiality, integrity and availability. This framework combines a set of technology mechanisms with environmental controls, and is shown to be sufficient to counter commonly encountered network security threats adequately.

  9. A Comparison of Geographic Information Systems, Complex Networks, and Other Models for Analyzing Transportation Network Topologies

    NASA Technical Reports Server (NTRS)

    Alexandrov, Natalia (Technical Monitor); Kuby, Michael; Tierney, Sean; Roberts, Tyler; Upchurch, Christopher

    2005-01-01

    This report reviews six classes of models that are used for studying transportation network topologies. The report is motivated by two main questions. First, what can the "new science" of complex networks (scale-free, small-world networks) contribute to our understanding of transport network structure, compared to more traditional methods? Second, how can geographic information systems (GIS) contribute to studying transport networks? The report defines terms that can be used to classify different kinds of models by their function, composition, mechanism, spatial and temporal dimensions, certainty, linearity, and resolution. Six broad classes of models for analyzing transport network topologies are then explored: GIS; static graph theory; complex networks; mathematical programming; simulation; and agent-based modeling. Each class of models is defined and classified according to the attributes introduced earlier. The paper identifies some typical types of research questions about network structure that have been addressed by each class of model in the literature.

  10. Information jet: Handling noisy big data from weakly disconnected network

    NASA Astrophysics Data System (ADS)

    Aurongzeb, Deeder

    Sudden aggregation (information jet) of large amount of data is ubiquitous around connected social networks, driven by sudden interacting and non-interacting events, network security threat attacks, online sales channel etc. Clustering of information jet based on time series analysis and graph theory is not new but little work is done to connect them with particle jet statistics. We show pre-clustering based on context can element soft network or network of information which is critical to minimize time to calculate results from noisy big data. We show difference between, stochastic gradient boosting and time series-graph clustering. For disconnected higher dimensional information jet, we use Kallenberg representation theorem (Kallenberg, 2005, arXiv:1401.1137) to identify and eliminate jet similarities from dense or sparse graph.

  11. Stoichiometric network theory for nonequilibrium biochemical systems.

    PubMed

    Qian, Hong; Beard, Daniel A; Liang, Shou-dan

    2003-02-01

    We introduce the basic concepts and develop a theory for nonequilibrium steady-state biochemical systems applicable to analyzing large-scale complex isothermal reaction networks. In terms of the stoichiometric matrix, we demonstrate both Kirchhoff's flux law sigma(l)J(l)=0 over a biochemical species, and potential law sigma(l) mu(l)=0 over a reaction loop. They reflect mass and energy conservation, respectively. For each reaction, its steady-state flux J can be decomposed into forward and backward one-way fluxes J = J+ - J-, with chemical potential difference deltamu = RT ln(J-/J+). The product -Jdeltamu gives the isothermal heat dissipation rate, which is necessarily non-negative according to the second law of thermodynamics. The stoichiometric network theory (SNT) embodies all of the relevant fundamental physics. Knowing J and deltamu of a biochemical reaction, a conductance can be computed which directly reflects the level of gene expression for the particular enzyme. For sufficiently small flux a linear relationship between J and deltamu can be established as the linear flux-force relation in irreversible thermodynamics, analogous to Ohm's law in electrical circuits.

  12. Information Processing Theory: Classroom Applications.

    ERIC Educational Resources Information Center

    Slate, John R.; Charlesworth, John R., Jr.

    1989-01-01

    Utilizes the information processing model of human memory to provide teachers with suggestions for improving the teaching-learning process. Briefly explains and specifies applications of major theoretical concepts: attention, active learning, meaningfulness, organization, advanced organizers, memory aids, overlearning, automatically, and…

  13. Information Theory and the Earth's Density Distribution

    NASA Technical Reports Server (NTRS)

    Rubincam, D. P.

    1979-01-01

    An argument for using the information theory approach as an inference technique in solid earth geophysics. A spherically symmetric density distribution is derived as an example of the method. A simple model of the earth plus knowledge of its mass and moment of inertia lead to a density distribution which was surprisingly close to the optimum distribution. Future directions for the information theory approach in solid earth geophysics as well as its strengths and weaknesses are discussed.

  14. Information theory and the earth's density distribution

    NASA Technical Reports Server (NTRS)

    Rubincam, D. P.

    1978-01-01

    The present paper argues for using the information theory approach as an inference technique in solid earth geophysics. A spherically symmetric density distribution is derived as an example of the method. A simple model of the earth plus knowledge of its mass and moment of inertia leads to a density distribution. Future directions for the information theory approach in solid earth geophysics as well as its strengths and weaknesses are discussed.

  15. Predicting Information Flows in Network Traffic.

    ERIC Educational Resources Information Center

    Hinich, Melvin J.; Molyneux, Robert E.

    2003-01-01

    Discusses information flow in networks and predicting network traffic and describes a study that uses time series analysis on a day's worth of Internet log data. Examines nonlinearity and traffic invariants, and suggests that prediction of network traffic may not be possible with current techniques. (Author/LRW)

  16. Theorising big IT programmes in healthcare: strong structuration theory meets actor-network theory.

    PubMed

    Greenhalgh, Trisha; Stones, Rob

    2010-05-01

    The UK National Health Service is grappling with various large and controversial IT programmes. We sought to develop a sharper theoretical perspective on the question "What happens - at macro-, meso- and micro-level - when government tries to modernise a health service with the help of big IT?" Using examples from data fragments at the micro-level of clinical work, we considered how structuration theory and actor-network theory (ANT) might be combined to inform empirical investigation. Giddens (1984) argued that social structures and human agency are recursively linked and co-evolve. ANT studies the relationships that link people and technologies in dynamic networks. It considers how discourses become inscribed in data structures and decision models of software, making certain network relations irreversible. Stones' (2005) strong structuration theory (SST) is a refinement of Giddens' work, systematically concerned with empirical research. It views human agents as linked in dynamic networks of position-practices. A quadripartite approcach considers [a] external social structures (conditions for action); [b] internal social structures (agents' capabilities and what they 'know' about the social world); [c] active agency and actions and [d] outcomes as they feed back on the position-practice network. In contrast to early structuration theory and ANT, SST insists on disciplined conceptual methodology and linking this with empirical evidence. In this paper, we adapt SST for the study of technology programmes, integrating elements from material interactionism and ANT. We argue, for example, that the position-practice network can be a socio-technical one in which technologies in conjunction with humans can be studied as 'actants'. Human agents, with their complex socio-cultural frames, are required to instantiate technology in social practices. Structurally relevant properties inscribed and embedded in technological artefacts constrain and enable human agency. The fortunes

  17. Theorising big IT programmes in healthcare: strong structuration theory meets actor-network theory.

    PubMed

    Greenhalgh, Trisha; Stones, Rob

    2010-05-01

    The UK National Health Service is grappling with various large and controversial IT programmes. We sought to develop a sharper theoretical perspective on the question "What happens - at macro-, meso- and micro-level - when government tries to modernise a health service with the help of big IT?" Using examples from data fragments at the micro-level of clinical work, we considered how structuration theory and actor-network theory (ANT) might be combined to inform empirical investigation. Giddens (1984) argued that social structures and human agency are recursively linked and co-evolve. ANT studies the relationships that link people and technologies in dynamic networks. It considers how discourses become inscribed in data structures and decision models of software, making certain network relations irreversible. Stones' (2005) strong structuration theory (SST) is a refinement of Giddens' work, systematically concerned with empirical research. It views human agents as linked in dynamic networks of position-practices. A quadripartite approcach considers [a] external social structures (conditions for action); [b] internal social structures (agents' capabilities and what they 'know' about the social world); [c] active agency and actions and [d] outcomes as they feed back on the position-practice network. In contrast to early structuration theory and ANT, SST insists on disciplined conceptual methodology and linking this with empirical evidence. In this paper, we adapt SST for the study of technology programmes, integrating elements from material interactionism and ANT. We argue, for example, that the position-practice network can be a socio-technical one in which technologies in conjunction with humans can be studied as 'actants'. Human agents, with their complex socio-cultural frames, are required to instantiate technology in social practices. Structurally relevant properties inscribed and embedded in technological artefacts constrain and enable human agency. The fortunes

  18. The Social Side of Information Networking.

    ERIC Educational Resources Information Center

    Katz, James E.

    1997-01-01

    Explores the social issues, including manners, security, crime (fraud), and social control associated with information networking, with emphasis on the Internet. Also addresses the influence of cellular phones, the Internet and other information technologies on society. (GR)

  19. A Security Architecture for Health Information Networks

    PubMed Central

    Kailar, Rajashekar

    2007-01-01

    Health information network security needs to balance exacting security controls with practicality, and ease of implementation in today’s healthcare enterprise. Recent work on ‘nationwide health information network’ architectures has sought to share highly confidential data over insecure networks such as the Internet. Using basic patterns of health network data flow and trust models to support secure communication between network nodes, we abstract network security requirements to a core set to enable secure inter-network data sharing. We propose a minimum set of security controls that can be implemented without needing major new technologies, but yet realize network security and privacy goals of confidentiality, integrity and availability. This framework combines a set of technology mechanisms with environmental controls, and is shown to be sufficient to counter commonly encountered network security threats adequately. PMID:18693862

  20. The application of information theory to biochemical signaling systems

    PubMed Central

    Rhee, Alex; Cheong, Raymond; Levchenko, Andre

    2012-01-01

    Cell signaling can be thought of fundamentally as an information transmission problem in which chemical messengers relay information about the external environment to the decision centers within a cell. Due to the biochemical nature of cellular signal transduction networks, molecular noise will inevitably limit the fidelity of any messages received and processed by a cell’s signal transduction networks, leaving it with an imperfect impression of its environment. Fortunately, Shannon’s information theory provides a mathematical framework independent of network complexity that can quantify the amount of information that can be transmitted despite biochemical noise. In particular, the channel capacity can be used to measure the maximum number of stimuli a cell can distinguish based upon the noisy responses of its signaling systems. Here, we provide a primer for quantitative biologists that covers fundamental concepts of information theory, highlights several key considerations when experimentally measuring channel capacity, and describes successful examples of the application of information theoretic analysis to biological signaling. PMID:22872091

  1. Informational Benefits via Knowledge Networks among Farmers

    ERIC Educational Resources Information Center

    Sligo, F. X.; Massey, Claire; Lewis, Kate

    2005-01-01

    Purpose: This research aimed to obtain insights into how farmers on small and medium-sized farms perceived the benefits of the information they receive from their interpersonal networks and other sources. Design/methodology/approach: Farmers' information environments were explored using socio-spatial knowledge networks, diaries and in-depth…

  2. Information Services in the International Network Marketplace.

    ERIC Educational Resources Information Center

    Hepworth, Mark E.

    1987-01-01

    Examines the internationalism of the network marketplace through case studies of the London Stock Exchange and I. P. Sharp Associates, a Canadian computer service bureau. Discussion focuses on the importance of transnational computer networks to the production of information services and marketplace expansion, and global information policy issues.…

  3. Information Network on Rural Development (INRD), Bangladesh.

    ERIC Educational Resources Information Center

    Wanasundra, Leelangi

    1994-01-01

    Discusses information networking in Bangladesh and describes the formation of the Information Network on Rural Development (INRD) which was initiated by the Center on Integrated Rural Development for Asia and the Pacific (CIRDAP). Organization, membership, activities, participation, and finance are examined. (four references) (LRW)

  4. Biological impacts and context of network theory

    SciTech Connect

    Almaas, E

    2007-01-05

    Many complex systems can be represented and analyzed as networks, and examples that have benefited from this approach span the natural sciences. For instance, we now know that systems as disparate as the World-Wide Web, the Internet, scientific collaborations, food webs, protein interactions and metabolism all have common features in their organization, the most salient of which are their scale-free connectivity distributions and their small-world behavior. The recent availability of large scale datasets that span the proteome or metabolome of an organism have made it possible to elucidate some of the organizational principles and rules that govern their function, robustness and evolution. We expect that combining the currently separate layers of information from gene regulatory-, signal transduction-, protein interaction- and metabolic networks will dramatically enhance our understanding of cellular function and dynamics.

  5. Conditioned reinforcement and information theory reconsidered.

    PubMed

    Shahan, Timothy A; Cunningham, Paul

    2015-03-01

    The idea that stimuli might function as conditioned reinforcers because of the information they convey about primary reinforcers has a long history in the study of learning. However, formal application of information theory to conditioned reinforcement has been largely abandoned in modern theorizing because of its failures with respect to observing behavior. In this paper we show how recent advances in the application of information theory to Pavlovian conditioning offer a novel approach to conditioned reinforcement. The critical feature of this approach is that calculations of information are based on reductions of uncertainty about expected time to primary reinforcement signaled by a conditioned reinforcer. Using this approach, we show that previous failures of information theory with observing behavior can be remedied, and that the resulting framework produces predictions similar to Delay Reduction Theory in both observing-response and concurrent-chains procedures. We suggest that the similarity of these predictions might offer an analytically grounded reason for why Delay Reduction Theory has been a successful theory of conditioned reinforcement. Finally, we suggest that the approach provides a formal basis for the assertion that conditioned reinforcement results from Pavlovian conditioning and may provide an integrative approach encompassing both domains.

  6. Conditioned reinforcement and information theory reconsidered.

    PubMed

    Shahan, Timothy A; Cunningham, Paul

    2015-03-01

    The idea that stimuli might function as conditioned reinforcers because of the information they convey about primary reinforcers has a long history in the study of learning. However, formal application of information theory to conditioned reinforcement has been largely abandoned in modern theorizing because of its failures with respect to observing behavior. In this paper we show how recent advances in the application of information theory to Pavlovian conditioning offer a novel approach to conditioned reinforcement. The critical feature of this approach is that calculations of information are based on reductions of uncertainty about expected time to primary reinforcement signaled by a conditioned reinforcer. Using this approach, we show that previous failures of information theory with observing behavior can be remedied, and that the resulting framework produces predictions similar to Delay Reduction Theory in both observing-response and concurrent-chains procedures. We suggest that the similarity of these predictions might offer an analytically grounded reason for why Delay Reduction Theory has been a successful theory of conditioned reinforcement. Finally, we suggest that the approach provides a formal basis for the assertion that conditioned reinforcement results from Pavlovian conditioning and may provide an integrative approach encompassing both domains. PMID:25766452

  7. Federal Information in the Networked Environment: A Perspective from the Coalition for Networked Information.

    ERIC Educational Resources Information Center

    Cheverie, Joan F.

    1999-01-01

    Discusses the development of strategies for providing access to and services for U.S. federal government information in higher education using the global information infrastructure, from the perspective of the Coalition for Networked Information (CNI). Discusses the preservation of electronic information and networked information discovery and…

  8. Information Processing Theory and Conceptual Development.

    ERIC Educational Resources Information Center

    Schroder, H. M.

    An educational program based upon information processing theory has been developed at Southern Illinois University. The integrating theme was the development of conceptual ability for coping with social and personal problems. It utilized student information search and concept formation as foundations for discussion and judgment and was organized…

  9. Connectivism and Information Literacy: Moving from Learning Theory to Pedagogical Practice

    ERIC Educational Resources Information Center

    Transue, Beth M.

    2013-01-01

    Connectivism is an emerging learning theory positing that knowledge comprises networked relationships and that learning comprises the ability to successfully navigate through these networks. Successful pedagogical strategies involve the instructor helping students to identify, navigate, and evaluate information from their learning networks. Many…

  10. Topological rubber elasticity theory. II. SCL networks

    NASA Astrophysics Data System (ADS)

    Iwata, Kazuyoshi

    1982-06-01

    The theory presented in part I [Iwata, J. Chem. Phys. 76, 6363 (1982)] is applied to networks having a simple-cubic-lattice (SCL) regular connection pattern, for which the projection matrix Γ* is computed easily. Derivatives of elastic free energies in regard to parameter λ for macroscopic deformation ∂F˜e/∂λ are computed numerically for isotropic deformations (swelling or deswelling) and for simple deformations (extension or contraction under swelling by α times). The initial arrangement of junction points r0 is assumed to be exactly SCL, and δ = d0/√νb is chosen as one of parameters in the calculation, where d0 is an end-to-end distance of the strands at the time of network formation, ν is a degree of polymerization in regard to the strands, and b is a statistical length per monomer. A repeating cell is chosen as a cube composed of 3×3×3 ( = 27) junction points and 3×27 ( = 81) strands. The following are found in this work. (1) Among four terms ∂F0,ph/∂λ, ∂F˜0,top/∂λ, ∂F˜1/∂λ, and ∂F˜2/∂λ of the derivative of the elastic free energy, the principal term is ∂F˜0,top/∂λ, which comes from the topological interaction among the strands; the phantom network term ∂F0,ph/∂λ is only a small correction to the net stress. (2) In isotropic deformations, the elastic free energy takes a minimum at λ0, a little below λ = 1; for compression below λ0, a strong postitive inner pressure, which comes from the topological repulsive forces among the strands, arises. (3) In simple deformations, the Mooney-Rivlin term appears for unswollen systems and it disappears as swelling of the network proceeds. Experimental plans are proposed which will reveal the existence of the topological repulsive interactions in the networks.

  11. Hamiltonian theory of symmetric optical network transforms

    NASA Astrophysics Data System (ADS)

    Törmä, Päivi; Stenholm, Stig

    1995-12-01

    We discuss the theory of extracting an interaction Hamiltonian from a preassigned unitary transformation of quantum states. Such a procedure is of significance in quantum computations and other optical information processing tasks. We particularize the problem to the construction of totally symmetric 2N ports as introduced by Zeilinger and his collaborators [A. Zeilinger, M. Zukowski, M. A. Horne, H. J. Bernstein, and D. M. Greenberger, in Fundamental Aspects of Quantum Theory, edited by J. Anandan and J. J. Safko (World Scientific, Singapore, 1994)]. These are realized by the discrete Fourier transform, which simplifies the construction of the Hamiltonian by known methods of linear algebra. The Hamiltonians found are discussed and alternative realizations of the Zeilinger class transformations are presented. We briefly discuss the applicability of the method to more general devices.

  12. Qualia Could Arise from Information Processing in Local Cortical Networks

    PubMed Central

    Orpwood, Roger

    2013-01-01

    Re-entrant feedback, either within sensory cortex or arising from prefrontal areas, has been strongly linked to the emergence of consciousness, both in theoretical and experimental work. This idea, together with evidence for local micro-consciousness, suggests the generation of qualia could in some way result from local network activity under re-entrant activation. This paper explores the possibility by examining the processing of information by local cortical networks. It highlights the difference between the information structure (how the information is physically embodied), and the information message (what the information is about). It focuses on the network’s ability to recognize information structures amongst its inputs under conditions of extensive local feedback, and to then assign information messages to those structures. It is shown that if the re-entrant feedback enables the network to achieve an attractor state, then the message assigned in any given pass of information through the network is a representation of the message assigned in the previous pass-through of information. Based on this ability the paper argues that as information is repeatedly cycled through the network, the information message that is assigned evolves from a recognition of what the input structure is, to what it is like, to how it appears, to how it seems. It could enable individual networks to be the site of qualia generation. The paper goes on to show networks in cortical layers 2/3 and 5a have the connectivity required for the behavior proposed, and reviews some evidence for a link between such local cortical cyclic activity and conscious percepts. It concludes with some predictions based on the theory discussed. PMID:23504586

  13. Reasonable fermionic quantum information theories require relativity

    NASA Astrophysics Data System (ADS)

    Friis, Nicolai

    2016-03-01

    We show that any quantum information theory based on anticommuting operators must be supplemented by a superselection rule deeply rooted in relativity to establish a reasonable notion of entanglement. While quantum information may be encoded in the fermionic Fock space, the unrestricted theory has a peculiar feature: the marginals of bipartite pure states need not have identical entropies, which leads to an ambiguous definition of entanglement. We solve this problem, by proving that it is removed by relativity, i.e., by the parity superselection rule that arises from Lorentz invariance via the spin-statistics connection. Our results hence unveil a fundamental conceptual inseparability of quantum information and the causal structure of relativistic field theory.

  14. Hierarchical social networks and information flow

    NASA Astrophysics Data System (ADS)

    López, Luis; F. F. Mendes, Jose; Sanjuán, Miguel A. F.

    2002-12-01

    Using a simple model for the information flow on social networks, we show that the traditional hierarchical topologies frequently used by companies and organizations, are poorly designed in terms of efficiency. Moreover, we prove that this type of structures are the result of the individual aim of monopolizing as much information as possible within the network. As the information is an appropriate measurement of centrality, we conclude that this kind of topology is so attractive for leaders, because the global influence each actor has within the network is completely determined by the hierarchical level occupied.

  15. Information theory, spectral geometry, and quantum gravity.

    PubMed

    Kempf, Achim; Martin, Robert

    2008-01-18

    We show that there exists a deep link between the two disciplines of information theory and spectral geometry. This allows us to obtain new results on a well-known quantum gravity motivated natural ultraviolet cutoff which describes an upper bound on the spatial density of information. Concretely, we show that, together with an infrared cutoff, this natural ultraviolet cutoff beautifully reduces the path integral of quantum field theory on curved space to a finite number of ordinary integrations. We then show, in particular, that the subsequent removal of the infrared cutoff is safe.

  16. Random subspaces in quantum information theory

    NASA Astrophysics Data System (ADS)

    Hayden, Patrick

    2005-03-01

    The selection of random unitary transformations plays a role in quantum information theory analogous to the role of random hash functions in classical information theory. Recent applications have included protocols achieving the quantum channel capacity and methods for extending superdense coding from bits to qubits. In addition, the corresponding random subspaces have proved useful for studying the structure of bipartite and multipartite entanglement. In quantum information theory, we're fond of saying that Hilbert space is a big place, the implication being that there's room for the unexpected to occur. The goal of this talk is to further bolster this homespun wisdowm. I'm going to present a number of results in quantum information theory that stem from the initially counterintuitive geometry of high-dimensional vector spaces, where subspaces with highly extremal properties are the norm rather than the exception. Peter Shor has shown, for example, that randomly selected subspaces can be used to send quantum information through a noisy quantum channel at the highest possible rate, that is, the quantum channel capacity. More recently, Debbie Leung, Andreas Winter and I demonstrated that a randomly chosen subspace of a bipartite quantum system will likely contain nothing but nearly maximally entangled states, even if the subspace is nearly as large as the original system in qubit terms. This observation has implications for communication, especially superdense coding.

  17. How Might Better Network Theories Support School Leadership Research?

    ERIC Educational Resources Information Center

    Hadfield, Mark; Jopling, Michael

    2012-01-01

    This article explores how recent research in education has applied different aspects of "network" theory to the study of school leadership. Constructs from different network theories are often used because of their perceived potential to clarify two perennial issues in leadership research. The first is the relative importance of formal and…

  18. Informal networks: the company behind the chart.

    PubMed

    Krackhardt, D; Hanson, J R

    1993-01-01

    A glance at an organizational chart can show who's the boss and who reports to whom. But this formal chart won't reveal which people confer on technical matters or discuss office politics over lunch. Much of the real work in any company gets done through this informal organization with its complex networks of relationships that cross functions and divisions. According to consultants David Krackhardt and Jeffrey Hanson, managers can harness the true power in their companies by diagramming three types of networks: the advice network, which reveals the people to whom others turn to get work done; the trust network, which uncovers who shares delicate information; and the communication network, which shows who talks about work-related matters. Using employee questionnaires, managers can generate network maps that will get to the root of many organizational problems. When a task force in a computer company, for example, was not achieving its goals, the CEO turned to network maps to find out why. He discovered that the task force leader was central in the advice network but marginal in the trust network. Task force members did not believe he would look out for their interests, so the CEO used the trust map to find someone to share responsibility for the group. And when a bank manager saw in the network map that there was little communication between tellers and supervisors, he looked for ways to foster interaction among employees of all levels. As companies continue to flatten and rely on teams, managers must rely less on their authority and more on understanding these informal networks. Managers who can use maps to identify, leverage, and revamp informal networks will have the key to success.

  19. The Teen Health Information Network (THINK).

    ERIC Educational Resources Information Center

    Kuzel, Judith; Erickson, Su

    1995-01-01

    Discusses the Teen Health Information Network (THINK), a grant-funded partnership of Aurora, Illinois, public libraries, schools, and community agencies to provide materials, information, and programming on issues related to teen health. Seven appendixes provide detailed information on survey results, collection evaluation and development,…

  20. Introduction to spiking neural networks: Information processing, learning and applications.

    PubMed

    Ponulak, Filip; Kasinski, Andrzej

    2011-01-01

    The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.

  1. Nonlinear adaptive networks: A little theory, a few applications

    SciTech Connect

    Jones, R.D.; Qian, S.; Barnes, C.W.; Bisset, K.R.; Bruce, G.M.; Lee, K.; Lee, L.A.; Mead, W.C.; O'Rourke, M.K.; Thode, L.E. ); Lee, Y.C.; Flake, G.W. Maryland Univ., College Park, MD ); Poli, I.J. Bologna Univ. )

    1990-01-01

    We present the theory of nonlinear adaptive networks and discuss a few applications. In particular, we review the theory of feedforward backpropagation networks. We than present the theory of the Connectionist Normalized Linear Spline network in both its feedforward and iterated modes. Also, we briefly discuss the theory of stochastic cellular automata. We then discuss applications to chaotic time series tidal prediction in Venice Lagoon, sonar transient detection, control of nonlinear processes, balancing a double inverted pendulum and design advice for free electron lasers. 26 refs., 23 figs.

  2. A Network for Physics Information.

    ERIC Educational Resources Information Center

    Koch, H. William; Herschman, Arthur

    The American Institute of Physics is working toward the development of a national information system for physics, whose objective is the organization of the flow of physics information from the producers to the users. The complete physics information system has several constituent subsystems, among which are: one for the management of the flow of…

  3. Equity trees and graphs via information theory

    NASA Astrophysics Data System (ADS)

    Harré, M.; Bossomaier, T.

    2010-01-01

    We investigate the similarities and differences between two measures of the relationship between equities traded in financial markets. Our measures are the correlation coefficients and the mutual information. In the context of financial markets correlation coefficients are well established whereas mutual information has not previously been as well studied despite its theoretically appealing properties. We show that asset trees which are derived from either the correlation coefficients or the mutual information have a mixture of both similarities and differences at the individual equity level and at the macroscopic level. We then extend our consideration from trees to graphs using the "genus 0" condition recently introduced in order to study the networks of equities.

  4. The Embedded Self: A Social Networks Approach to Identity Theory

    ERIC Educational Resources Information Center

    Walker, Mark H.; Lynn, Freda B.

    2013-01-01

    Despite the fact that key sociological theories of self and identity view the self as fundamentally rooted in networks of interpersonal relationships, empirical research investigating how personal network structure influences the self is conspicuously lacking. To address this gap, we examine links between network structure and role identity…

  5. Pain: A Distributed Brain Information Network?

    PubMed Central

    Mano, Hiroaki; Seymour, Ben

    2015-01-01

    Understanding how pain is processed in the brain has been an enduring puzzle, because there doesn't appear to be a single “pain cortex” that directly codes the subjective perception of pain. An emerging concept is that, instead, pain might emerge from the coordinated activity of an integrated brain network. In support of this view, Woo and colleagues present evidence that distinct brain networks support the subjective changes in pain that result from nociceptive input and self-directed cognitive modulation. This evidence for the sensitivity of distinct neural subsystems to different aspects of pain opens up the way to more formal computational network theories of pain. PMID:25562782

  6. Engaging Theories and Models to Inform Practice

    ERIC Educational Resources Information Center

    Kraus, Amanda

    2012-01-01

    Helping students prepare for the complex transition to life after graduation is an important responsibility shared by those in student affairs and others in higher education. This chapter explores theories and models that can inform student affairs practitioners and faculty in preparing students for life after college. The focus is on roles,…

  7. 78 FR 17418 - Rural Health Information Technology Network Development Grant

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-03-21

    ... HUMAN SERVICES Health Resources and Services Administration Rural Health Information Technology Network... award under the Rural Health Information Technology Network Development Grant (RHITND) to Grace... relinquishing its fiduciary responsibilities for the Rural Health Information Technology Network...

  8. Reverse engineering cellular networks with information theoretic methods.

    PubMed

    Villaverde, Alejandro F; Ross, John; Banga, Julio R

    2013-01-01

    Building mathematical models of cellular networks lies at the core of systems biology. It involves, among other tasks, the reconstruction of the structure of interactions between molecular components, which is known as network inference or reverse engineering. Information theory can help in the goal of extracting as much information as possible from the available data. A large number of methods founded on these concepts have been proposed in the literature, not only in biology journals, but in a wide range of areas. Their critical comparison is difficult due to the different focuses and the adoption of different terminologies. Here we attempt to review some of the existing information theoretic methodologies for network inference, and clarify their differences. While some of these methods have achieved notable success, many challenges remain, among which we can mention dealing with incomplete measurements, noisy data, counterintuitive behaviour emerging from nonlinear relations or feedback loops, and computational burden of dealing with large data sets.

  9. Origin of cells and network information.

    PubMed

    Tanabe, Shihori

    2015-04-26

    All cells are derived from one cell, and the origin of different cell types is a subject of curiosity. Cells construct life through appropriately timed networks at each stage of development. Communication among cells and intracellular signaling are essential for cell differentiation and for life processes. Cellular molecular networks establish cell diversity and life. The investigation of the regulation of each gene in the genome within the cellular network is therefore of interest. Stem cells produce various cells that are suitable for specific purposes. The dynamics of the information in the cellular network changes as the status of cells is altered. The components of each cell are subject to investigation.

  10. Origin of cells and network information

    PubMed Central

    Tanabe, Shihori

    2015-01-01

    All cells are derived from one cell, and the origin of different cell types is a subject of curiosity. Cells construct life through appropriately timed networks at each stage of development. Communication among cells and intracellular signaling are essential for cell differentiation and for life processes. Cellular molecular networks establish cell diversity and life. The investigation of the regulation of each gene in the genome within the cellular network is therefore of interest. Stem cells produce various cells that are suitable for specific purposes. The dynamics of the information in the cellular network changes as the status of cells is altered. The components of each cell are subject to investigation. PMID:25914760

  11. Quantification of image quality using information theory.

    PubMed

    Niimi, Takanaga; Maeda, Hisatoshi; Ikeda, Mitsuru; Imai, Kuniharu

    2011-12-01

    Aims of present study were to examine usefulness of information theory in visual assessment of image quality. We applied first order approximation of the Shannon's information theory to compute information losses (IL). Images of a contrast-detail mammography (CDMAM) phantom were acquired with computed radiographies for various radiation doses. Information content was defined as the entropy Σp( i )log(1/p ( i )), in which detection probabilities p ( i ) were calculated from distribution of detection rate of the CDMAM. IL was defined as the difference between information content and information obtained. IL decreased with increases in the disk diameters (P < 0.0001, ANOVA) and in the radiation doses (P < 0.002, F-test). Sums of IL, which we call total information losses (TIL), were closely correlated with the image quality figures (r = 0.985). TIL was dependent on the distribution of image reading ability of each examinee, even when average reading ratio was the same in the group. TIL was shown to be sensitive to the observers' distribution of image readings and was expected to improve the evaluation of image quality.

  12. Information theory in living systems, methods, applications, and challenges.

    PubMed

    Gatenby, Robert A; Frieden, B Roy

    2007-02-01

    Living systems are distinguished in nature by their ability to maintain stable, ordered states far from equilibrium. This is despite constant buffeting by thermodynamic forces that, if unopposed, will inevitably increase disorder. Cells maintain a steep transmembrane entropy gradient by continuous application of information that permits cellular components to carry out highly specific tasks that import energy and export entropy. Thus, the study of information storage, flow and utilization is critical for understanding first principles that govern the dynamics of life. Initial biological applications of information theory (IT) used Shannon's methods to measure the information content in strings of monomers such as genes, RNA, and proteins. Recent work has used bioinformatic and dynamical systems to provide remarkable insights into the topology and dynamics of intracellular information networks. Novel applications of Fisher-, Shannon-, and Kullback-Leibler informations are promoting increased understanding of the mechanisms by which genetic information is converted to work and order. Insights into evolution may be gained by analysis of the the fitness contributions from specific segments of genetic information as well as the optimization process in which the fitness are constrained by the substrate cost for its storage and utilization. Recent IT applications have recognized the possible role of nontraditional information storage structures including lipids and ion gradients as well as information transmission by molecular flux across cell membranes. Many fascinating challenges remain, including defining the intercellular information dynamics of multicellular organisms and the role of disordered information storage and flow in disease. PMID:17083004

  13. Insights into the organization of biochemical regulatory networks using graph theory analyses.

    PubMed

    Ma'ayan, Avi

    2009-02-27

    Graph theory has been a valuable mathematical modeling tool to gain insights into the topological organization of biochemical networks. There are two types of insights that may be obtained by graph theory analyses. The first provides an overview of the global organization of biochemical networks; the second uses prior knowledge to place results from multivariate experiments, such as microarray data sets, in the context of known pathways and networks to infer regulation. Using graph analyses, biochemical networks are found to be scale-free and small-world, indicating that these networks contain hubs, which are proteins that interact with many other molecules. These hubs may interact with many different types of proteins at the same time and location or at different times and locations, resulting in diverse biological responses. Groups of components in networks are organized in recurring patterns termed network motifs such as feedback and feed-forward loops. Graph analysis revealed that negative feedback loops are less common and are present mostly in proximity to the membrane, whereas positive feedback loops are highly nested in an architecture that promotes dynamical stability. Cell signaling networks have multiple pathways from some input receptors and few from others. Such topology is reminiscent of a classification system. Signaling networks display a bow-tie structure indicative of funneling information from extracellular signals and then dispatching information from a few specific central intracellular signaling nexuses. These insights show that graph theory is a valuable tool for gaining an understanding of global regulatory features of biochemical networks.

  14. Insights into the organization of biochemical regulatory networks using graph theory analyses.

    PubMed

    Ma'ayan, Avi

    2009-02-27

    Graph theory has been a valuable mathematical modeling tool to gain insights into the topological organization of biochemical networks. There are two types of insights that may be obtained by graph theory analyses. The first provides an overview of the global organization of biochemical networks; the second uses prior knowledge to place results from multivariate experiments, such as microarray data sets, in the context of known pathways and networks to infer regulation. Using graph analyses, biochemical networks are found to be scale-free and small-world, indicating that these networks contain hubs, which are proteins that interact with many other molecules. These hubs may interact with many different types of proteins at the same time and location or at different times and locations, resulting in diverse biological responses. Groups of components in networks are organized in recurring patterns termed network motifs such as feedback and feed-forward loops. Graph analysis revealed that negative feedback loops are less common and are present mostly in proximity to the membrane, whereas positive feedback loops are highly nested in an architecture that promotes dynamical stability. Cell signaling networks have multiple pathways from some input receptors and few from others. Such topology is reminiscent of a classification system. Signaling networks display a bow-tie structure indicative of funneling information from extracellular signals and then dispatching information from a few specific central intracellular signaling nexuses. These insights show that graph theory is a valuable tool for gaining an understanding of global regulatory features of biochemical networks. PMID:18940806

  15. A Preliminary Theory of Dark Network Resilience

    ERIC Educational Resources Information Center

    Bakker, Rene M.; Raab, Jorg; Milward, H. Brinton

    2012-01-01

    A crucial contemporary policy question for governments across the globe is how to cope with international crime and terrorist networks. Many such "dark" networks--that is, networks that operate covertly and illegally--display a remarkable level of resilience when faced with shocks and attacks. Based on an in-depth study of three cases (MK, the…

  16. Reinforce Networking Theory with OPNET Simulation

    ERIC Educational Resources Information Center

    Guo, Jinhua; Xiang, Weidong; Wang, Shengquan

    2007-01-01

    As networking systems have become more complex and expensive, hands-on experiments based on networking simulation have become essential for teaching the key computer networking topics to students. The simulation approach is the most cost effective and highly useful because it provides a virtual environment for an assortment of desirable features…

  17. Network Anomaly Detection System with Optimized DS Evidence Theory

    PubMed Central

    Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu

    2014-01-01

    Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network—complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each senor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly. PMID:25254258

  18. Weight-Control Information Network

    MedlinePlus

    ... applicants; human subjects research information; grant review and management resources; and commonly used funding mechanisms, including diversity and small business programs Research Programs & Contacts Research program and staff contacts for ...

  19. Ohio Valley Community Health Information Network.

    ERIC Educational Resources Information Center

    Guard, Roger; And Others

    The Ohio Valley Community Health Information Network (OVCHIN) works to determine the efficacy of delivering health information to residents of rural southern Ohio and the urban and suburban Cincinnati area. OVCHIN is a community-based, consumer-defined demonstration grant program funded by the National Telecommunications and Information…

  20. Child Rights Information Network Newsletter, 1996.

    ERIC Educational Resources Information Center

    Purbrick, Becky, Ed.

    1996-01-01

    These two newsletter issues communicate activities of the newly formed Child Rights Information Network (CRIN) and report on emerging information resources and activities concerning children and child rights. The January 1996 issue describes the history of CRIN, provides updates on the activities of projects linked to CRIN, and summarizes…

  1. Protecting Personal Information on Social Networking Sites

    ERIC Educational Resources Information Center

    Gallant, David T.

    2011-01-01

    Almost everyone uses social networking sites like Facebook, MySpace, and LinkedIn. Since Facebook is the most popular site in the history of the Internet, this article will focus on how one can protect his/her personal information and how that extends to protecting the private information of others.

  2. Distributing Executive Information Systems through Networks.

    ERIC Educational Resources Information Center

    Penrod, James I.; And Others

    1993-01-01

    Many colleges and universities will soon adopt distributed systems for executive information and decision support. Distribution of shared information through computer networks will improve decision-making processes dramatically on campuses. Critical success factors include administrative support, favorable organizational climate, ease of use,…

  3. Information transfer in community structured multiplex networks

    NASA Astrophysics Data System (ADS)

    Solé Ribalta, Albert; Granell, Clara; Gómez, Sergio; Arenas, Alex

    2015-08-01

    The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.). The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

  4. Clinical information systems for integrated healthcare networks.

    PubMed Central

    Teich, J. M.

    1998-01-01

    In the 1990's, a large number of hospitals and medical practices have merged to form integrated healthcare networks (IHN's). The nature of an IHN creates new demands for information management, and also imposes new constraints on information systems for the network. Important tradeoffs must be made between homogeneity and flexibility, central and distributed governance, and access and confidentiality. This paper describes key components of clinical information systems for IHN's, and examines important design decisions that affect the value of such systems. Images Figure 1 PMID:9929178

  5. A theory of maximizing sensory information.

    PubMed

    van Hateren, J H

    1992-01-01

    A theory is developed on the assumption that early sensory processing aims at maximizing the information rate in the channels connecting the sensory system to more central parts of the brain, where it is assumed that these channels are noisy and have a limited dynamic range. Given a stimulus power spectrum, the theory enables the computation of filters accomplishing this maximizing of information. Resulting filters are band-pass or high-pass at high signal-to-noise ratios, and low-pass at low signal-to-noise ratios. In spatial vision this corresponds to lateral inhibition and pooling, respectively. The filters comply with Weber's law over a considerable range of signal-to-noise ratios.

  6. Comparing cosmic web classifiers using information theory

    NASA Astrophysics Data System (ADS)

    Leclercq, Florent; Lavaux, Guilhem; Jasche, Jens; Wandelt, Benjamin

    2016-08-01

    We introduce a decision scheme for optimally choosing a classifier, which segments the cosmic web into different structure types (voids, sheets, filaments, and clusters). Our framework, based on information theory, accounts for the design aims of different classes of possible applications: (i) parameter inference, (ii) model selection, and (iii) prediction of new observations. As an illustration, we use cosmographic maps of web-types in the Sloan Digital Sky Survey to assess the relative performance of the classifiers T-WEB, DIVA and ORIGAMI for: (i) analyzing the morphology of the cosmic web, (ii) discriminating dark energy models, and (iii) predicting galaxy colors. Our study substantiates a data-supported connection between cosmic web analysis and information theory, and paves the path towards principled design of analysis procedures for the next generation of galaxy surveys. We have made the cosmic web maps, galaxy catalog, and analysis scripts used in this work publicly available.

  7. The theory of pattern formation on directed networks

    NASA Astrophysics Data System (ADS)

    Asllani, Malbor; Challenger, Joseph D.; Pavone, Francesco Saverio; Sacconi, Leonardo; Fanelli, Duccio

    2014-07-01

    Dynamical processes on networks have generated widespread interest in recent years. The theory of pattern formation in reaction-diffusion systems defined on symmetric networks has often been investigated, due to its applications in a wide range of disciplines. Here we extend the theory to the case of directed networks, which are found in a number of different fields, such as neuroscience, computer networks and traffic systems. Owing to the structure of the network Laplacian, the dispersion relation has both real and imaginary parts, at variance with the case for a symmetric, undirected network. The homogeneous fixed point can become unstable due to the topology of the network, resulting in a new class of instabilities, which cannot be induced on undirected graphs. Results from a linear stability analysis allow the instability region to be analytically traced. Numerical simulations show travelling waves, or quasi-stationary patterns, depending on the characteristics of the underlying graph.

  8. The decoupling approach to quantum information theory

    NASA Astrophysics Data System (ADS)

    Dupuis, Frédéric

    2010-04-01

    Quantum information theory studies the fundamental limits that physical laws impose on information processing tasks such as data compression and data transmission on noisy channels. This thesis presents general techniques that allow one to solve many fundamental problems of quantum information theory in a unified framework. The central theorem of this thesis proves the existence of a protocol that transmits quantum data that is partially known to the receiver through a single use of an arbitrary noisy quantum channel. In addition to the intrinsic interest of this problem, this theorem has as immediate corollaries several central theorems of quantum information theory. The following chapters use this theorem to prove the existence of new protocols for two other types of quantum channels, namely quantum broadcast channels and quantum channels with side information at the transmitter. These protocols also involve sending quantum information partially known by the receiver with a single use of the channel, and have as corollaries entanglement-assisted and unassisted asymptotic coding theorems. The entanglement-assisted asymptotic versions can, in both cases, be considered as quantum versions of the best coding theorems known for the classical versions of these problems. The last chapter deals with a purely quantum phenomenon called locking. We demonstrate that it is possible to encode a classical message into a quantum state such that, by removing a subsystem of logarithmic size with respect to its total size, no measurement can have significant correlations with the message. The message is therefore "locked" by a logarithmic-size key. This thesis presents the first locking protocol for which the success criterion is that the trace distance between the joint distribution of the message and the measurement result and the product of their marginals be sufficiently small.

  9. Information spreading on dynamic social networks

    NASA Astrophysics Data System (ADS)

    Liu, Chuang; Zhang, Zi-Ke

    2014-04-01

    Nowadays, information spreading on social networks has triggered an explosive attention in various disciplines. Most of previous works in this area mainly focus on discussing the effects of spreading probability or immunization strategy on static networks. However, in real systems, the peer-to-peer network structure changes constantly according to frequently social activities of users. In order to capture this dynamical property and study its impact on information spreading, in this paper, a link rewiring strategy based on the Fermi function is introduced. In the present model, the informed individuals tend to break old links and reconnect to their second-order friends with more uninformed neighbors. Simulation results on the susceptible-infected-recovered (SIR) model with fixed recovery time T=1 indicate that the information would spread more faster and broader with the proposed rewiring strategy. Extensive analyses of the information cascade size distribution show that the spreading process of the initial steps plays a very important role, that is to say, the information will spread out if it is still survival at the beginning time. The proposed model may shed some light on the in-depth understanding of information spreading on dynamical social networks.

  10. Computerized and Networked Government Information.

    ERIC Educational Resources Information Center

    Stratford, Jean Slemmons; Stratford, Juri

    1997-01-01

    Discusses developments at the U.S. Bureau of the Census. Includes strategies and changes for Census 2000, new information storage and retrieval systems, a new Internet subscription service offering enhanced access to selected Census Bureau databases, and the transfer of responsibility for the Census of Agriculture to the National Agricultural…

  11. Fisheries Information Network in Indonesia.

    ERIC Educational Resources Information Center

    Balachandran, Sarojini

    During the early 1980s the Indonesian government made a policy decision to develop fisheries as an important sector of the national economy. In doing so, it recognized the need for the collection and dissemination of fisheries research information not only for the scientists themselves, but also for the ultimate transfer of technology through…

  12. Computerized and Networked Government Information.

    ERIC Educational Resources Information Center

    Stratford, Jean Slemmons; Stratford, Juri

    1998-01-01

    The United States has taken only piecemeal steps to ensure privacy of personal information. This article examines the U.S. relating to privacy and data protection. It defines privacy and discusses international agreements relating to privacy, federal data protection laws, and narrowly applicable laws. (AEF)

  13. Networks in financial markets based on the mutual information rate.

    PubMed

    Fiedor, Paweł

    2014-05-01

    In the last few years there have been many efforts in econophysics studying how network theory can facilitate understanding of complex financial markets. These efforts consist mainly of the study of correlation-based hierarchical networks. This is somewhat surprising as the underlying assumptions of research looking at financial markets are that they are complex systems and thus behave in a nonlinear manner, which is confirmed by numerous studies, making the use of correlations which are inherently dealing with linear dependencies only baffling. In this paper we introduce a way to incorporate nonlinear dynamics and dependencies into hierarchical networks to study financial markets using mutual information and its dynamical extension: the mutual information rate. We show that this approach leads to different results than the correlation-based approach used in most studies, on the basis of 91 companies listed on the New York Stock Exchange 100 between 2003 and 2013, using minimal spanning trees and planar maximally filtered graphs.

  14. Networks in financial markets based on the mutual information rate

    NASA Astrophysics Data System (ADS)

    Fiedor, Paweł

    2014-05-01

    In the last few years there have been many efforts in econophysics studying how network theory can facilitate understanding of complex financial markets. These efforts consist mainly of the study of correlation-based hierarchical networks. This is somewhat surprising as the underlying assumptions of research looking at financial markets are that they are complex systems and thus behave in a nonlinear manner, which is confirmed by numerous studies, making the use of correlations which are inherently dealing with linear dependencies only baffling. In this paper we introduce a way to incorporate nonlinear dynamics and dependencies into hierarchical networks to study financial markets using mutual information and its dynamical extension: the mutual information rate. We show that this approach leads to different results than the correlation-based approach used in most studies, on the basis of 91 companies listed on the New York Stock Exchange 100 between 2003 and 2013, using minimal spanning trees and planar maximally filtered graphs.

  15. Information processing in convex operational theories

    SciTech Connect

    Barnum, Howard Nelch; Wilce, Alexander G

    2008-01-01

    In order to understand the source and extent of the greater-than-classical information processing power of quantum systems, one wants to characterize both classical and quantum mechanics as points in a broader space of possible theories. One approach to doing this, pioneered by Abramsky and Coecke, is to abstract the essential categorical features of classical and quantum mechanics that support various information-theoretic constraints and possibilities, e.g., the impossibility of cloning in the latter, and the possibility of teleportation in both. Another approach, pursued by the authors and various collaborators, is to begin with a very conservative, and in a sense very concrete, generalization of classical probability theory--which is still sufficient to encompass quantum theory--and to ask which 'quantum' informational phenomena can be reproduced in this much looser setting. In this paper, we review the progress to date in this second programme, and offer some suggestions as to how to link it with the categorical semantics for quantum processes developed by Abramsky and Coecke.

  16. Common cold outbreaks: A network theory approach

    NASA Astrophysics Data System (ADS)

    Vishkaie, Faranak Rajabi; Bakouie, Fatemeh; Gharibzadeh, Shahriar

    2014-11-01

    In this study, at first we evaluated the network structure in social encounters by which respiratory diseases can spread. We considered common-cold and recorded a sample of human population and actual encounters between them. Our results show that the database structure presents a great value of clustering. In the second step, we evaluated dynamics of disease spread with SIR model by assigning a function to each node of the structural network. The rate of disease spread in networks was observed to be inversely correlated with characteristic path length. Therefore, the shortcuts have a significant role in increasing spread rate. We conclude that the dynamics of social encounters' network stands between the random and the lattice in network spectrum. Although in this study we considered the period of common-cold disease for network dynamics, it seems that similar approaches may be useful for other airborne diseases such as SARS.

  17. Minimum energy information fusion in sensor networks

    SciTech Connect

    Chapline, G

    1999-05-11

    In this paper we consider how to organize the sharing of information in a distributed network of sensors and data processors so as to provide explanations for sensor readings with minimal expenditure of energy. We point out that the Minimum Description Length principle provides an approach to information fusion that is more naturally suited to energy minimization than traditional Bayesian approaches. In addition we show that for networks consisting of a large number of identical sensors Kohonen self-organization provides an exact solution to the problem of combing the sensor outputs into minimal description length explanations.

  18. Groups, information theory, and Einstein's likelihood principle

    NASA Astrophysics Data System (ADS)

    Sicuro, Gabriele; Tempesta, Piergiulio

    2016-04-01

    We propose a unifying picture where the notion of generalized entropy is related to information theory by means of a group-theoretical approach. The group structure comes from the requirement that an entropy be well defined with respect to the composition of independent systems, in the context of a recently proposed generalization of the Shannon-Khinchin axioms. We associate to each member of a large class of entropies a generalized information measure, satisfying the additivity property on a set of independent systems as a consequence of the underlying group law. At the same time, we also show that Einstein's likelihood function naturally emerges as a byproduct of our informational interpretation of (generally nonadditive) entropies. These results confirm the adequacy of composable entropies both in physical and social science contexts.

  19. Groups, information theory, and Einstein's likelihood principle.

    PubMed

    Sicuro, Gabriele; Tempesta, Piergiulio

    2016-04-01

    We propose a unifying picture where the notion of generalized entropy is related to information theory by means of a group-theoretical approach. The group structure comes from the requirement that an entropy be well defined with respect to the composition of independent systems, in the context of a recently proposed generalization of the Shannon-Khinchin axioms. We associate to each member of a large class of entropies a generalized information measure, satisfying the additivity property on a set of independent systems as a consequence of the underlying group law. At the same time, we also show that Einstein's likelihood function naturally emerges as a byproduct of our informational interpretation of (generally nonadditive) entropies. These results confirm the adequacy of composable entropies both in physical and social science contexts.

  20. Groups, information theory, and Einstein's likelihood principle.

    PubMed

    Sicuro, Gabriele; Tempesta, Piergiulio

    2016-04-01

    We propose a unifying picture where the notion of generalized entropy is related to information theory by means of a group-theoretical approach. The group structure comes from the requirement that an entropy be well defined with respect to the composition of independent systems, in the context of a recently proposed generalization of the Shannon-Khinchin axioms. We associate to each member of a large class of entropies a generalized information measure, satisfying the additivity property on a set of independent systems as a consequence of the underlying group law. At the same time, we also show that Einstein's likelihood function naturally emerges as a byproduct of our informational interpretation of (generally nonadditive) entropies. These results confirm the adequacy of composable entropies both in physical and social science contexts. PMID:27176234

  1. Possibilistic systems within a general information theory

    SciTech Connect

    Joslyn, C.

    1999-06-01

    The author surveys possibilistic systems theory and place it in the context of Imprecise Probabilities and General Information Theory (GIT). In particular, he argues that possibilistic systems hold a distinct position within a broadly conceived, synthetic GIT. The focus is on systems and applications which are semantically grounded by empirical measurement methods (statistical counting), rather than epistemic or subjective knowledge elicitation or assessment methods. Regarding fuzzy measures as special provisions, and evidence measures (belief and plausibility measures) as special fuzzy measures, thereby he can measure imprecise probabilities directly and empirically from set-valued frequencies (random set measurement). More specifically, measurements of random intervals yield empirical fuzzy intervals. In the random set (Dempster-Shafer) context, probability and possibility measures stand as special plausibility measures in that their distributionality (decomposability) maps directly to an aggregable structure of the focal classes of their random sets. Further, possibility measures share with imprecise probabilities the ability to better handle open world problems where the universe of discourse is not specified in advance. In addition to empirically grounded measurement methods, possibility theory also provides another crucial component of a full systems theory, namely prediction methods in the form of finite (Markov) processes which are also strictly analogous to the probabilistic forms.

  2. MIDER: network inference with mutual information distance and entropy reduction.

    PubMed

    Villaverde, Alejandro F; Ross, John; Morán, Federico; Banga, Julio R

    2014-01-01

    The prediction of links among variables from a given dataset is a task referred to as network inference or reverse engineering. It is an open problem in bioinformatics and systems biology, as well as in other areas of science. Information theory, which uses concepts such as mutual information, provides a rigorous framework for addressing it. While a number of information-theoretic methods are already available, most of them focus on a particular type of problem, introducing assumptions that limit their generality. Furthermore, many of these methods lack a publicly available implementation. Here we present MIDER, a method for inferring network structures with information theoretic concepts. It consists of two steps: first, it provides a representation of the network in which the distance among nodes indicates their statistical closeness. Second, it refines the prediction of the existing links to distinguish between direct and indirect interactions and to assign directionality. The method accepts as input time-series data related to some quantitative features of the network nodes (such as e.g. concentrations, if the nodes are chemical species). It takes into account time delays between variables, and allows choosing among several definitions and normalizations of mutual information. It is general purpose: it may be applied to any type of network, cellular or otherwise. A Matlab implementation including source code and data is freely available (http://www.iim.csic.es/~gingproc/mider.html). The performance of MIDER has been evaluated on seven different benchmark problems that cover the main types of cellular networks, including metabolic, gene regulatory, and signaling. Comparisons with state of the art information-theoretic methods have demonstrated the competitive performance of MIDER, as well as its versatility. Its use does not demand any a priori knowledge from the user; the default settings and the adaptive nature of the method provide good results for a wide

  3. MIDER: network inference with mutual information distance and entropy reduction.

    PubMed

    Villaverde, Alejandro F; Ross, John; Morán, Federico; Banga, Julio R

    2014-01-01

    The prediction of links among variables from a given dataset is a task referred to as network inference or reverse engineering. It is an open problem in bioinformatics and systems biology, as well as in other areas of science. Information theory, which uses concepts such as mutual information, provides a rigorous framework for addressing it. While a number of information-theoretic methods are already available, most of them focus on a particular type of problem, introducing assumptions that limit their generality. Furthermore, many of these methods lack a publicly available implementation. Here we present MIDER, a method for inferring network structures with information theoretic concepts. It consists of two steps: first, it provides a representation of the network in which the distance among nodes indicates their statistical closeness. Second, it refines the prediction of the existing links to distinguish between direct and indirect interactions and to assign directionality. The method accepts as input time-series data related to some quantitative features of the network nodes (such as e.g. concentrations, if the nodes are chemical species). It takes into account time delays between variables, and allows choosing among several definitions and normalizations of mutual information. It is general purpose: it may be applied to any type of network, cellular or otherwise. A Matlab implementation including source code and data is freely available (http://www.iim.csic.es/~gingproc/mider.html). The performance of MIDER has been evaluated on seven different benchmark problems that cover the main types of cellular networks, including metabolic, gene regulatory, and signaling. Comparisons with state of the art information-theoretic methods have demonstrated the competitive performance of MIDER, as well as its versatility. Its use does not demand any a priori knowledge from the user; the default settings and the adaptive nature of the method provide good results for a wide

  4. An Attractor Network in the Hippocampus: Theory and Neurophysiology

    ERIC Educational Resources Information Center

    Rolls, Edmund T.

    2007-01-01

    A quantitative computational theory of the operation of the CA3 system as an attractor or autoassociation network is described. Based on the proposal that CA3-CA3 autoassociative networks are important for episodic or event memory in which space is a component (place in rodents and spatial view in primates), it has been shown behaviorally that the…

  5. Astrophysical data analysis with information field theory

    SciTech Connect

    Enßlin, Torsten

    2014-12-05

    Non-parametric imaging and data analysis in astrophysics and cosmology can be addressed by information field theory (IFT), a means of Bayesian, data based inference on spatially distributed signal fields. IFT is a statistical field theory, which permits the construction of optimal signal recovery algorithms. It exploits spatial correlations of the signal fields even for nonlinear and non-Gaussian signal inference problems. The alleviation of a perception threshold for recovering signals of unknown correlation structure by using IFT will be discussed in particular as well as a novel improvement on instrumental self-calibration schemes. IFT can be applied to many areas. Here, applications in in cosmology (cosmic microwave background, large-scale structure) and astrophysics (galactic magnetism, radio interferometry) are presented.

  6. Information theory applications for biological sequence analysis.

    PubMed

    Vinga, Susana

    2014-05-01

    Information theory (IT) addresses the analysis of communication systems and has been widely applied in molecular biology. In particular, alignment-free sequence analysis and comparison greatly benefited from concepts derived from IT, such as entropy and mutual information. This review covers several aspects of IT applications, ranging from genome global analysis and comparison, including block-entropy estimation and resolution-free metrics based on iterative maps, to local analysis, comprising the classification of motifs, prediction of transcription factor binding sites and sequence characterization based on linguistic complexity and entropic profiles. IT has also been applied to high-level correlations that combine DNA, RNA or protein features with sequence-independent properties, such as gene mapping and phenotype analysis, and has also provided models based on communication systems theory to describe information transmission channels at the cell level and also during evolutionary processes. While not exhaustive, this review attempts to categorize existing methods and to indicate their relation with broader transversal topics such as genomic signatures, data compression and complexity, time series analysis and phylogenetic classification, providing a resource for future developments in this promising area.

  7. Social Capital Theory: Implications for Women's Networking and Learning

    ERIC Educational Resources Information Center

    Alfred, Mary V.

    2009-01-01

    This chapter describes social capital theory as a framework for exploring women's networking and social capital resources. It presents the foundational assumptions of the theory, the benefits and risks of social capital engagement, a feminist critique of social capital, and the role of social capital in adult learning.

  8. BOOK REVIEW: Theory of Neural Information Processing Systems

    NASA Astrophysics Data System (ADS)

    Galla, Tobias

    2006-04-01

    It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 1011 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kühn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the

  9. Information Filtering on Coupled Social Networks

    PubMed Central

    Nie, Da-Cheng; Zhang, Zi-Ke; Zhou, Jun-Lin; Fu, Yan; Zhang, Kui

    2014-01-01

    In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks. PMID:25003525

  10. Information transfer network of global market indices

    NASA Astrophysics Data System (ADS)

    Kim, Yup; Kim, Jinho; Yook, Soon-Hyung

    2015-07-01

    We study the topological properties of the information transfer networks (ITN) of the global financial market indices for six different periods. ITN is a directed weighted network, in which the direction and weight are determined by the transfer entropy between market indices. By applying the threshold method, it is found that ITN undergoes a crossover from the complete graph to a small-world (SW) network. SW regime of ITN for a global crisis is found to be much more enhanced than that for ordinary periods. Furthermore, when ITN is in SW regime, the average clustering coefficient is found to be synchronized with average volatility of markets. We also compare the results with the topological properties of correlation networks.

  11. Optimal Network Modularity for Information Diffusion

    NASA Astrophysics Data System (ADS)

    Nematzadeh, Azadeh; Ferrara, Emilio; Flammini, Alessandro; Ahn, Yong-Yeol

    2014-08-01

    We investigate the impact of community structure on information diffusion with the linear threshold model. Our results demonstrate that modular structure may have counterintuitive effects on information diffusion when social reinforcement is present. We show that strong communities can facilitate global diffusion by enhancing local, intracommunity spreading. Using both analytic approaches and numerical simulations, we demonstrate the existence of an optimal network modularity, where global diffusion requires the minimal number of early adopters.

  12. Networked Information Resources. SPEC Kit 253.

    ERIC Educational Resources Information Center

    Bleiler, Richard, Comp.; Plum, Terry, Comp.

    1999-01-01

    This SPEC Kit, published six times per year, examines how Association of Research Libraries (ARL) libraries have structured themselves to identify networked information resources in the market, to evaluate them for purchase, to make purchasing decisions, to publicize them, and to assess their continued utility. In the summer of 1999, the survey…

  13. OASIS: Prototyping Graphical Interfaces to Networked Information.

    ERIC Educational Resources Information Center

    Buckland, Michael K.; And Others

    1993-01-01

    Describes the latest modifications being made to OASIS, a front-end enhancement to the University of California's MELVYL online union catalog. Highlights include the X Windows interface; multiple database searching to act as an information network; Lisp implementation for flexible data representation; and OASIS commands and features to help…

  14. MIDER: Network Inference with Mutual Information Distance and Entropy Reduction

    PubMed Central

    Villaverde, Alejandro F.; Ross, John; Morán, Federico; Banga, Julio R.

    2014-01-01

    The prediction of links among variables from a given dataset is a task referred to as network inference or reverse engineering. It is an open problem in bioinformatics and systems biology, as well as in other areas of science. Information theory, which uses concepts such as mutual information, provides a rigorous framework for addressing it. While a number of information-theoretic methods are already available, most of them focus on a particular type of problem, introducing assumptions that limit their generality. Furthermore, many of these methods lack a publicly available implementation. Here we present MIDER, a method for inferring network structures with information theoretic concepts. It consists of two steps: first, it provides a representation of the network in which the distance among nodes indicates their statistical closeness. Second, it refines the prediction of the existing links to distinguish between direct and indirect interactions and to assign directionality. The method accepts as input time-series data related to some quantitative features of the network nodes (such as e.g. concentrations, if the nodes are chemical species). It takes into account time delays between variables, and allows choosing among several definitions and normalizations of mutual information. It is general purpose: it may be applied to any type of network, cellular or otherwise. A Matlab implementation including source code and data is freely available (http://www.iim.csic.es/~gingproc/mider.html). The performance of MIDER has been evaluated on seven different benchmark problems that cover the main types of cellular networks, including metabolic, gene regulatory, and signaling. Comparisons with state of the art information–theoretic methods have demonstrated the competitive performance of MIDER, as well as its versatility. Its use does not demand any a priori knowledge from the user; the default settings and the adaptive nature of the method provide good results for a wide

  15. Information spread in networks: Games, optimal control, and stabilization

    NASA Astrophysics Data System (ADS)

    Khanafer, Ali

    This thesis focuses on designing efficient mechanisms for controlling information spread in networks. We consider two models for information spread. The first one is the well-known distributed averaging dynamics. The second model is a nonlinear one that describes virus spread in computer and biological networks. We seek to design optimal, robust, and stabilizing controllers under practical constraints. For distributed averaging networks, we study the interaction between a network designer and an adversary. We consider two types of attacks on the network. In Attack-I, the adversary strategically disconnects a set of links to prevent the nodes from reaching consensus. Meanwhile, the network designer assists the nodes in reaching consensus by changing the weights of a limited number of links in the network. We formulate two problems to describe this competition where the order in which the players act is reversed in the two problems. Although the canonical equations provided by the Pontryagin's Maximum Principle (MP) seem to be intractable, we provide an alternative characterization for the optimal strategies that makes connection to potential theory. Further, we provide a sufficient condition for the existence of a saddle-point equilibrium (SPE) for the underlying zero-sum game. In Attack-II, the designer and the adversary are both capable of altering the measurements of all nodes in the network by injecting global signals. We impose two constraints on both players: a power constraint and an energy constraint. We assume that the available energy to each player is not sufficient to operate at maximum power throughout the horizon of the game. We show the existence of an SPE and derive the optimal strategies in closed form for this attack scenario. As an alternative to the "network designer vs. adversary" framework, we investigate the possibility of stabilizing unknown network diffusion processes using a distributed mechanism, where the uncertainty is due to an attack

  16. An information theory of image gathering

    NASA Technical Reports Server (NTRS)

    Fales, Carl L.; Huck, Friedrich O.

    1991-01-01

    Shannon's mathematical theory of communication is extended to image gathering. Expressions are obtained for the total information that is received with a single image-gathering channel and with parallel channels. It is concluded that the aliased signal components carry information even though these components interfere with the within-passband components in conventional image gathering and restoration, thereby degrading the fidelity and visual quality of the restored image. An examination of the expression for minimum mean-square-error, or Wiener-matrix, restoration from parallel image-gathering channels reveals a method for unscrambling the within-passband and aliased signal components to restore spatial frequencies beyond the sampling passband out to the spatial frequency response cutoff of the optical aperture.

  17. The need-informational theory of emotions.

    PubMed

    Simonov, P V

    1984-03-01

    As an evolvement of Pavlov ideas on higher nervous (psychic) activity 'the need-informational theory of emotions' was suggested by the author in 1964. According to it an emotion is a function of two major factors: (1) power and quality of actual need (or drive, or motivation) and (2) estimation of probability (possibility) of need satisfaction on the basis of phylo- and ontogenetic experience. In the process of experimental testing of 'the need-informational theory of emotions' the role of different cerebral structures (frontal neocortex, hippocampus, amygdala, hypothalamus) in the genesis of emotional states and in the organization of goal-directed behavior was elucidated. The experimental data showed that these 4 brain structures play the major role in estimation of signals coming from environment and in the choice of subject's reactions. The individual characteristics of the interaction between the 4 brain structures must be taken into consideration in discussing neurophysiological backgrounds of different types of the higher nervous activity (temperaments), parameters of extra-introversion and neurotism (emotionality), the formation of main types of neurosis.

  18. Information security trades in tactical wireless networks

    NASA Astrophysics Data System (ADS)

    Kurdziel, Michael T.; Alvermann, John A.

    2015-05-01

    Wireless networks are now ubiquitous across the tactical environment. They offer unprecedented communications and data access capabilities. However, providing information security to wireless transmissions without impacting performance is a challenge. The information security requirement for each operational scenario presents a large trade space for functionality versus performance. One aspect of this trade space pertains to where information security services are integrated into the protocol stack. This paper will present an overview of the various options that exist and will discuss the advantages and disadvantages of each option.

  19. Information diffusion in structured online social networks

    NASA Astrophysics Data System (ADS)

    Li, Pei; Zhang, Yini; Qiao, Fengcai; Wang, Hui

    2015-05-01

    Nowadays, due to the word-of-mouth effect, online social networks have been considered to be efficient approaches to conduct viral marketing, which makes it of great importance to understand the diffusion dynamics in online social networks. However, most research on diffusion dynamics in epidemiology and existing social networks cannot be applied directly to characterize online social networks. In this paper, we propose models to characterize the information diffusion in structured online social networks with push-based forwarding mechanism. We introduce the term user influence to characterize the average number of times that messages are browsed which is incurred by a given type user generating a message, and study the diffusion threshold, above which the user influence of generating a message will approach infinity. We conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of use in understanding the diffusion dynamics in online social networks and also critical for advertisers in viral marketing who want to estimate the user influence before posting an advertisement.

  20. Improving information filtering via network manipulation

    NASA Astrophysics Data System (ADS)

    Zhang, Fuguo; Zeng, An

    2012-12-01

    The recommender system is a very promising way to address the problem of overabundant information for online users. Although the information filtering for the online commercial systems has received much attention recently, almost all of the previous works are dedicated to design new algorithms and consider the user-item bipartite networks as given and constant information. However, many problems for recommender systems such as the cold-start problem (i.e., low recommendation accuracy for the small-degree items) are actually due to the limitation of the underlying user-item bipartite networks. In this letter, we propose a strategy to enhance the performance of the already existing recommendation algorithms by directly manipulating the user-item bipartite networks, namely adding some virtual connections to the networks. Numerical analyses on two benchmark data sets, MovieLens and Netflix, show that our method can remarkably improves the recommendation performance. Specifically, it not only improves the recommendations accuracy (especially for the small-degree items), but also helps the recommender systems generate more diverse and novel recommendations.

  1. On the genesis of the idiotypic network theory.

    PubMed

    Civello, Andrea

    2013-01-01

    The idiotypic network theory (INT) was conceived by the Danish immunologist Niels Kaj Jerne in 1973/1974. It proposes an overall view of the immune system as a network of lymphocytes and antibodies. The paper tries to offer a reconstruction of the genesis of the theory, now generally discarded and of mostly historical interest, first of all, by taking into account the context in which Jerne's theoretical proposal was advanced. It is argued the theory challenged, in a sense, the supremacy of the clonal selection theory (CST), this being regarded as the predominant paradigm in the immunological scenario. As CST found shortcomings in explaining certain phenomena, anomalies, one could view INT as a competing paradigm claiming to be able to make sense of such phenomena in its own conceptual framework. After a summary outline of the historical background and some relevant terminological elucidations, a narrative of the various phases of elaboration of the theory is proposed, up to its official public presentation.

  2. Unravelling the Social Network: Theory and Research

    ERIC Educational Resources Information Center

    Merchant, Guy

    2012-01-01

    Despite the widespread popularity of social networking sites (SNSs) amongst children and young people in compulsory education, relatively little scholarly work has explored the fundamental issues at stake. This paper makes an original contribution to the field by locating the study of this online activity within the broader terrain of social…

  3. Realizing Wisdom Theory in Complex Learning Networks

    ERIC Educational Resources Information Center

    Kok, Ayse

    2009-01-01

    The word "wisdom" is rarely seen in contemporary technology and learning discourse. This conceptual paper aims to provide some clear principles that answer the question: How can we establish wisdom in complex learning networks? By considering the nature of contemporary calls for wisdom the paper provides a metatheoretial framework to evaluate the…

  4. Informal Theory: The Ignored Link in Theory-to-Practice

    ERIC Educational Resources Information Center

    Love, Patrick

    2012-01-01

    Applying theory to practice in student affairs is dominated by the assumption that formal theory is directly applied to practice. Among the problems with this assumption is that many practitioners believe they must choose between their lived experiences and formal theory, and that graduate students are taught that their experience "does not…

  5. Mapping Information Flow in Sensorimotor Networks

    PubMed Central

    Lungarella, Max; Sporns, Olaf

    2006-01-01

    Biological organisms continuously select and sample information used by their neural structures for perception and action, and for creating coherent cognitive states guiding their autonomous behavior. Information processing, however, is not solely an internal function of the nervous system. Here we show, instead, how sensorimotor interaction and body morphology can induce statistical regularities and information structure in sensory inputs and within the neural control architecture, and how the flow of information between sensors, neural units, and effectors is actively shaped by the interaction with the environment. We analyze sensory and motor data collected from real and simulated robots and reveal the presence of information structure and directed information flow induced by dynamically coupled sensorimotor activity, including effects of motor outputs on sensory inputs. We find that information structure and information flow in sensorimotor networks (a) is spatially and temporally specific; (b) can be affected by learning, and (c) can be affected by changes in body morphology. Our results suggest a fundamental link between physical embeddedness and information, highlighting the effects of embodied interactions on internal (neural) information processing, and illuminating the role of various system components on the generation of behavior. PMID:17069456

  6. Boundary Depth Information Using Hopfield Neural Network

    NASA Astrophysics Data System (ADS)

    Xu, Sheng; Wang, Ruisheng

    2016-06-01

    Depth information is widely used for representation, reconstruction and modeling of 3D scene. Generally two kinds of methods can obtain the depth information. One is to use the distance cues from the depth camera, but the results heavily depend on the device, and the accuracy is degraded greatly when the distance from the object is increased. The other one uses the binocular cues from the matching to obtain the depth information. It is more and more mature and convenient to collect the depth information of different scenes by stereo matching methods. In the objective function, the data term is to ensure that the difference between the matched pixels is small, and the smoothness term is to smooth the neighbors with different disparities. Nonetheless, the smoothness term blurs the boundary depth information of the object which becomes the bottleneck of the stereo matching. This paper proposes a novel energy function for the boundary to keep the discontinuities and uses the Hopfield neural network to solve the optimization. We first extract the region of interest areas which are the boundary pixels in original images. Then, we develop the boundary energy function to calculate the matching cost. At last, we solve the optimization globally by the Hopfield neural network. The Middlebury stereo benchmark is used to test the proposed method, and results show that our boundary depth information is more accurate than other state-of-the-art methods and can be used to optimize the results of other stereo matching methods.

  7. Hilbert's projective metric in quantum information theory

    NASA Astrophysics Data System (ADS)

    Reeb, David; Kastoryano, Michael J.; Wolf, Michael M.

    2011-08-01

    We introduce and apply Hilbert's projective metric in the context of quantum information theory. The metric is induced by convex cones such as the sets of positive, separable or positive partial transpose operators. It provides bounds on measures for statistical distinguishability of quantum states and on the decrease of entanglement under protocols involving local quantum operations and classical communication or under other cone-preserving operations. The results are formulated in terms of general cones and base norms and lead to contractivity bounds for quantum channels, for instance, improving Ruskai's trace-norm contraction inequality. A new duality between distinguishability measures and base norms is provided. For two given pairs of quantum states we show that the contraction of Hilbert's projective metric is necessary and sufficient for the existence of a probabilistic quantum operation that maps one pair onto the other. Inequalities between Hilbert's projective metric and the Chernoff bound, the fidelity and various norms are proven.

  8. Characterization of vehicle behavior with information theory

    NASA Astrophysics Data System (ADS)

    Aquino, Andre L. L.; Cavalcante, Tamer S. G.; Almeida, Eliana S.; Frery, Alejandro C.; Rosso, Osvaldo A.

    2015-10-01

    This work proposes the use of Information Theory for the characterization of vehicles behavior through their velocities. Three public data sets were used: (i) Mobile Century data set collected on Highway I-880, near Union City, California; (ii) Borlänge GPS data set collected in the Swedish city of Borlänge; and (iii) Beijing taxicabs data set collected in Beijing, China, where each vehicle speed is stored as a time series. The Bandt-Pompe methodology combined with the Complexity-Entropy plane were used to identify different regimes and behaviors. The global velocity is compatible with a correlated noise with f - k Power Spectrum with k ≥ 0. With this we identify traffic behaviors as, for instance, random velocities ( k ≃ 0) when there is congestion, and more correlated velocities ( k ≃ 3) in the presence of free traffic flow.

  9. Gauge Theory for the Rate Equations: Electrodynamics on a Network

    SciTech Connect

    Timm, Carsten

    2007-02-16

    Systems of coupled rate equations are ubiquitous in many areas of science, for example, in the description of electronic transport through quantum dots and molecules. They can be understood as a continuity equation expressing the conservation of probability. It is shown that this conservation law can be implemented by constructing a gauge theory akin to classical electrodynamics on the network of possible states described by the rate equations. The properties of this gauge theory are analyzed. It turns out that the network is maximally connected with respect to the electromagnetic fields even if the allowed transitions form a sparse network. It is found that the numbers of degrees of freedom of the electric and magnetic fields are equal. The results shed light on the structure of classical Abelian gauge theory beyond the particular motivation in terms of rate equations.

  10. Gauge theory for the rate equations: electrodynamics on a network.

    PubMed

    Timm, Carsten

    2007-02-16

    Systems of coupled rate equations are ubiquitous in many areas of science, for example, in the description of electronic transport through quantum dots and molecules. They can be understood as a continuity equation expressing the conservation of probability. It is shown that this conservation law can be implemented by constructing a gauge theory akin to classical electrodynamics on the network of possible states described by the rate equations. The properties of this gauge theory are analyzed. It turns out that the network is maximally connected with respect to the electromagnetic fields even if the allowed transitions form a sparse network. It is found that the numbers of degrees of freedom of the electric and magnetic fields are equal. The results shed light on the structure of classical Abelian gauge theory beyond the particular motivation in terms of rate equations.

  11. A unified data representation theory for network visualization, ordering and coarse-graining

    NASA Astrophysics Data System (ADS)

    Kovács, István A.; Mizsei, Réka; Csermely, Péter

    2015-09-01

    Representation of large data sets became a key question of many scientific disciplines in the last decade. Several approaches for network visualization, data ordering and coarse-graining accomplished this goal. However, there was no underlying theoretical framework linking these problems. Here we show an elegant, information theoretic data representation approach as a unified solution of network visualization, data ordering and coarse-graining. The optimal representation is the hardest to distinguish from the original data matrix, measured by the relative entropy. The representation of network nodes as probability distributions provides an efficient visualization method and, in one dimension, an ordering of network nodes and edges. Coarse-grained representations of the input network enable both efficient data compression and hierarchical visualization to achieve high quality representations of larger data sets. Our unified data representation theory will help the analysis of extensive data sets, by revealing the large-scale structure of complex networks in a comprehensible form.

  12. Extended Network Generalized Entanglement Theory: therapeutic mechanisms, empirical predictions, and investigations.

    PubMed

    Hyland, Michael E

    2003-12-01

    Extended Network Generalized Entanglement Theory (Entanglement Theory for short) combines two earlier theories based on complexity theory and quantum mechanics. The theory's assumptions are: the body is a complex, self-organizing system (the extended network) that self-organizes so as to achieve genetically defined patterns (where patterns include morphologic as well as lifestyle patterns). These pattern-specifying genes require feedback that is provided by generalized quantum entanglement. Additionally, generalized entanglement has evolved as a form of communication between people (and animals) and can be used in healing. Entanglement Theory suggests that several processes are involved in complementary and alternative medicine (CAM). Direct subtle therapy creates network change either through lifestyle management, some manual therapies, and psychologically mediated effects of therapy. Indirect subtle therapy is a process of entanglement with other people or physical entities (e.g., remedies, healing sites). Both types of subtle therapy create two kinds of information within the network--either that the network is more disregulated than it is and the network then compensates for this error, or as a guide for network change leading to healing. Most CAM therapies involve a combination of indirect and direct therapies, making empirical evaluation complex. Empirical predictions from this theory are contrasted with those from two other possible mechanisms of healing: (1) psychologic processes and (2) mechanisms involving electromagnetic influence between people (biofield/energy medicine). Topics for empirical study include a hyperfast communication system, the phenomenology of entanglement, predictors of outcome in naturally occurring clinical settings, and the importance of therapist and patient characteristics to outcome.

  13. Using information networks for competitive advantage.

    PubMed

    Rothenberg, R L

    1995-01-01

    Although the healthcare "information superhighway" has received considerable attention, the use of information technology to create a sustainable competitive advantage is not new to other industries. Economic survival in the new world of managed care may depend on a healthcare delivery system's ability to use network-based communications technologies to differentiate itself in the market, especially through cost savings and demonstration of desirable outcomes. The adaptability of these technologies can help position healthcare organizations to break the paradigms of the past and thrive in a market environment that stresses coordination, efficiency, and quality in various settings.

  14. Intra- Versus Intersex Aggression: Testing Theories of Sex Differences Using Aggression Networks.

    PubMed

    Wölfer, Ralf; Hewstone, Miles

    2015-08-01

    Two theories offer competing explanations of sex differences in aggressive behavior: sexual-selection theory and social-role theory. While each theory has specific strengths and limitations depending on the victim's sex, research hardly differentiates between intrasex and intersex aggression. In the present study, 11,307 students (mean age = 14.96 years; 50% girls, 50% boys) from 597 school classes provided social-network data (aggression and friendship networks) as well as physical (body mass index) and psychosocial (gender and masculinity norms) information. Aggression networks were used to disentangle intra- and intersex aggression, whereas their class-aggregated sex differences were analyzed using contextual predictors derived from sexual-selection and social-role theories. As expected, results revealed that sexual-selection theory predicted male-biased sex differences in intrasex aggression, whereas social-role theory predicted male-biased sex differences in intersex aggression. Findings suggest the value of explaining sex differences separately for intra- and intersex aggression with a dual-theory framework covering both evolutionary and normative components.

  15. Complex network theory, streamflow, and hydrometric monitoring system design

    NASA Astrophysics Data System (ADS)

    Halverson, M. J.; Fleming, S. W.

    2015-07-01

    Network theory is applied to an array of streamflow gauges located in the Coast Mountains of British Columbia (BC) and Yukon, Canada. The goal of the analysis is to assess whether insights from this branch of mathematical graph theory can be meaningfully applied to hydrometric data, and, more specifically, whether it may help guide decisions concerning stream gauge placement so that the full complexity of the regional hydrology is efficiently captured. The streamflow data, when represented as a complex network, have a global clustering coefficient and average shortest path length consistent with small-world networks, which are a class of stable and efficient networks common in nature, but the observed degree distribution did not clearly indicate a scale-free network. Stability helps ensure that the network is robust to the loss of nodes; in the context of a streamflow network, stability is interpreted as insensitivity to station removal at random. Community structure is also evident in the streamflow network. A network theoretic community detection algorithm identified separate communities, each of which appears to be defined by the combination of its median seasonal flow regime (pluvial, nival, hybrid, or glacial, which in this region in turn mainly reflects basin elevation) and geographic proximity to other communities (reflecting shared or different daily meteorological forcing). Furthermore, betweenness analyses suggest a handful of key stations which serve as bridges between communities and might be highly valued. We propose that an idealized sampling network should sample high-betweenness stations, small-membership communities which are by definition rare or undersampled relative to other communities, and index stations having large numbers of intracommunity links, while retaining some degree of redundancy to maintain network robustness.

  16. Social Network Theory in Engineering Education

    NASA Astrophysics Data System (ADS)

    Simon, Peter A.

    Collaborative groups are important both in the learning environment of engineering education and, in the real world, the business of engineering design. Selecting appropriate individuals to form an effective group and monitoring a group's progress are important aspects of successful task performance. This exploratory study looked at using the concepts of cognitive social structures, structural balance, and centrality from social network analysis as well as the measures of emotional intelligence. The concepts were used to analyze potential team members to examine if an individual's ability to perceive emotion in others and the self and to use, understand, and manage those emotions are a factor in a group's performance. The students from a capstone design course in computer engineering were used as volunteer subjects. They were formed into groups and assigned a design exercise to determine whether and which of the above-mentioned tools would be effective in both selecting teams and predicting the quality of the resultant design. The results were inconclusive with the exception of an individual's ability to accurately perceive emotions. The instruments that were successful were the Self-Monitoring scale and the accuracy scores derived from cognitive social structures and Level IV of network levels of analysis.

  17. Wireless network traffic modeling based on extreme value theory

    NASA Astrophysics Data System (ADS)

    Liu, Chunfeng; Shu, Yantai; Yang, Oliver W. W.; Liu, Jiakun; Dong, Linfang

    2006-10-01

    In this paper, Extreme Value Theory (EVT) is presented to analyze wireless network traffic. The role of EVT is to allow the development of procedures that are scientifically and statistically rational to estimate the extreme behavior of random processes. There are two primary methods for studying extremes: the Block Maximum (BM) method and the Points Over Threshold (POT) method. By taking limited traffic data that is greater than the threshold value, our experiment and analysis show the wireless network traffic model obtained with the EVT fits well with that of empirical distribution of traffic, thus illustrating that EVT has a good application foreground in the analysis of wireless network traffic.

  18. Optimal learning paths in information networks.

    PubMed

    Rodi, G C; Loreto, V; Servedio, V D P; Tria, F

    2015-01-01

    Each sphere of knowledge and information could be depicted as a complex mesh of correlated items. By properly exploiting these connections, innovative and more efficient navigation strategies could be defined, possibly leading to a faster learning process and an enduring retention of information. In this work we investigate how the topological structure embedding the items to be learned can affect the efficiency of the learning dynamics. To this end we introduce a general class of algorithms that simulate the exploration of knowledge/information networks standing on well-established findings on educational scheduling, namely the spacing and lag effects. While constructing their learning schedules, individuals move along connections, periodically revisiting some concepts, and sometimes jumping on very distant ones. In order to investigate the effect of networked information structures on the proposed learning dynamics we focused both on synthetic and real-world graphs such as subsections of Wikipedia and word-association graphs. We highlight the existence of optimal topological structures for the simulated learning dynamics whose efficiency is affected by the balance between hubs and the least connected items. Interestingly, the real-world graphs we considered lead naturally to almost optimal learning performances. PMID:26030508

  19. Optimal learning paths in information networks.

    PubMed

    Rodi, G C; Loreto, V; Servedio, V D P; Tria, F

    2015-01-01

    Each sphere of knowledge and information could be depicted as a complex mesh of correlated items. By properly exploiting these connections, innovative and more efficient navigation strategies could be defined, possibly leading to a faster learning process and an enduring retention of information. In this work we investigate how the topological structure embedding the items to be learned can affect the efficiency of the learning dynamics. To this end we introduce a general class of algorithms that simulate the exploration of knowledge/information networks standing on well-established findings on educational scheduling, namely the spacing and lag effects. While constructing their learning schedules, individuals move along connections, periodically revisiting some concepts, and sometimes jumping on very distant ones. In order to investigate the effect of networked information structures on the proposed learning dynamics we focused both on synthetic and real-world graphs such as subsections of Wikipedia and word-association graphs. We highlight the existence of optimal topological structures for the simulated learning dynamics whose efficiency is affected by the balance between hubs and the least connected items. Interestingly, the real-world graphs we considered lead naturally to almost optimal learning performances.

  20. Evaluating Action Learning: A Critical Realist Complex Network Theory Approach

    ERIC Educational Resources Information Center

    Burgoyne, John G.

    2010-01-01

    This largely theoretical paper will argue the case for the usefulness of applying network and complex adaptive systems theory to an understanding of action learning and the challenge it is evaluating. This approach, it will be argued, is particularly helpful in the context of improving capability in dealing with wicked problems spread around…

  1. Theory VI. Computational Materials Sciences Network (CMSN)

    SciTech Connect

    Zhang, Z Y

    2008-06-25

    The Computational Materials Sciences Network (CMSN) is a virtual center consisting of scientists interested in working together, across organizational and disciplinary boundaries, to formulate and pursue projects that reflect challenging and relevant computational research in the materials sciences. The projects appropriate for this center involve those problems best pursued through broad cooperative efforts, rather than those key problems best tackled by single investigator groups. CMSN operates similarly to the DOE Center of Excellence for the Synthesis and Processing of Advanced Materials, coordinated by George Samara at Sandia. As in the Synthesis and Processing Center, the intent of the modest funding for CMSN is to foster partnering and collective activities. All CMSN proposals undergo external peer review and are judged foremost on the quality and timeliness of the science and also on criteria relevant to the objective of the center, especially concerning a strategy for partnering. More details about CMSN can be found on the CMSN webpages at: http://cmpweb.ameslab.gov/ccms/CMSN-homepage.html.

  2. Domain theoretic structures in quantum information theory

    NASA Astrophysics Data System (ADS)

    Feng, Johnny

    2011-12-01

    In this thesis, we continue the study of domain theoretic structures in quantum information theory initiated by Keye Martin and Bob Coecke in 2002. The first part of the thesis is focused on exploring the domain theoretic properties of qubit channels. We discover that the Scott continuous qubit channels are exactly those that are unital or constant. We then prove that the unital qubit channels form a continuous dcpo, and identify various measurements on them. We show that Holevo capacity is a measurement on unital qubit channels, and discover the natural measurement in this setting. We find that qubit channels also form a continuous dcpo, but capacity fails to be a measurement. In the second part we focus on the study of exact dcpos, a domain theoretic structure, closely related to continuous dcpos, possessed by quantum states. Exact dcpos admit a topology, called the exact topology, and we show that the exact topology has an order theoretic characterization similar to the characterization of the Scott topology on continuous dcpos. We then explore the connection between exact and continuous dcpos; first, by identifying an important set of points, called the split points, that distinguishes between exact and continuous structures; second, by exploring a continuous completion of exact dcpos, and showing that we can recover the exact topology from the Scott topology of the completion.

  3. Critical Theory and Information Studies: A Marcusean Infusion

    ERIC Educational Resources Information Center

    Pyati, Ajit K.

    2006-01-01

    In the field of library and information science, also known as information studies, critical theory is often not included in debates about the discipline's theoretical foundations. This paper argues that the critical theory of Herbert Marcuse, in particular, has a significant contribution to make to the field of information studies. Marcuse's…

  4. Theory of rumour spreading in complex social networks

    NASA Astrophysics Data System (ADS)

    Nekovee, M.; Moreno, Y.; Bianconi, G.; Marsili, M.

    2007-01-01

    We introduce a general stochastic model for the spread of rumours, and derive mean-field equations that describe the dynamics of the model on complex social networks (in particular, those mediated by the Internet). We use analytical and numerical solutions of these equations to examine the threshold behaviour and dynamics of the model on several models of such networks: random graphs, uncorrelated scale-free networks and scale-free networks with assortative degree correlations. We show that in both homogeneous networks and random graphs the model exhibits a critical threshold in the rumour spreading rate below which a rumour cannot propagate in the system. In the case of scale-free networks, on the other hand, this threshold becomes vanishingly small in the limit of infinite system size. We find that the initial rate at which a rumour spreads is much higher in scale-free networks than in random graphs, and that the rate at which the spreading proceeds on scale-free networks is further increased when assortative degree correlations are introduced. The impact of degree correlations on the final fraction of nodes that ever hears a rumour, however, depends on the interplay between network topology and the rumour spreading rate. Our results show that scale-free social networks are prone to the spreading of rumours, just as they are to the spreading of infections. They are relevant to the spreading dynamics of chain emails, viral advertising and large-scale information dissemination algorithms on the Internet.

  5. Deep Space Network information system architecture study

    NASA Technical Reports Server (NTRS)

    Beswick, C. A.; Markley, R. W. (Editor); Atkinson, D. J.; Cooper, L. P.; Tausworthe, R. C.; Masline, R. C.; Jenkins, J. S.; Crowe, R. A.; Thomas, J. L.; Stoloff, M. J.

    1992-01-01

    The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control.

  6. The problem of applying information theory to efficient image transmission.

    NASA Technical Reports Server (NTRS)

    Sakrison, D. J.

    1973-01-01

    The main ideas of Shannon's (1948, 1960) theory of source encoding with a fidelity constraint, more commonly known as rate distortion theory, are summarized. The theory was specifically intended to provide a theoretical basis for efficient transmission of information such as images. What the theory has to contribute to the problem is demonstrated. Difficulties that impeded application of the theory to image transmission, and current efforts to solve these difficulties are discussed.

  7. Profile: the Philippine Population Information Network.

    PubMed

    1991-06-01

    The profile of Philippine Population Information Network (POPIN) is described in this article as having changed management structure from the Population Center Foundation to the Government's Population Commission, Information Management and Research Division (IMRD) in 1989. This restructuring resulted in the transfer in 1990 of the Department of Social Welfare and Development to the Office of the President. POPIN also serves Asia/Pacific POPIN. POPCOM makes policy and coordinates and monitors population activities. POPIN's goal is to improve the flow and utilization of population information nationwide. The National Population Library was moved in 1989 to the POPCOM Central Office Building and became the Philippine Information Center. The collection includes 6000 books, 400 research reports, and 4000 other documents (brochures, reprints, conference materials, and so on); 42 video tapes about the Philippine population program and a cassette player are available. In 1989, 14 regional centers were set up in POPCOM regional offices and designated Regional Population Information Centers. There are also school-based information centers operating as satellite information centers. The Regional and school-based centers serve the purpose of providing technical information through collection development, cataloguing, classification, storage and retrieval, and circulation. The target users are policy makers, government and private research agencies, researchers, and faculty and students. Publications developed and produced by the Center include the 3rd Supplement of the Union Catalogue of Population Literature, the 1987-88 Annotated Bibliography of Philippine Population Literature (PPL), the forthcoming 1989-90 edition of the Annotated Bibliography of PPL, and a biyearly newsletter, POPINEWS. Microcomputers have been acquired for the Regional Centers, with the idea of computerizing POPIN. Computer upgrading is also being done within the IMRD to provide POPLINE CD

  8. Trends in information theory-based chemical structure codification.

    PubMed

    Barigye, Stephen J; Marrero-Ponce, Yovani; Pérez-Giménez, Facundo; Bonchev, Danail

    2014-08-01

    This report offers a chronological review of the most relevant applications of information theory in the codification of chemical structure information, through the so-called information indices. Basically, these are derived from the analysis of the statistical patterns of molecular structure representations, which include primitive global chemical formulae, chemical graphs, or matrix representations. Finally, new approaches that attempt to go "back to the roots" of information theory, in order to integrate other information-theoretic measures in chemical structure coding are discussed.

  9. Essential elements of online information networks on invasive alien species

    USGS Publications Warehouse

    Simpson, A.; Sellers, E.; Grosse, A.; Xie, Y.

    2006-01-01

    In order to be effective, information must be placed in the proper context and organized in a manner that is logical and (preferably) standardized. Recently, invasive alien species (IAS) scientists have begun to create online networks to share their information concerning IAS prevention and control. At a special networking session at the Beijing International Symposium on Biological Invasions, an online Eastern Asia-North American IAS Information Network (EA-NA Network) was proposed. To prepare for the development of this network, and to provide models for other regional collaborations, we compare four examples of global, regional, and national online IAS information networks: the Global Invasive Species Information Network, the Invasives Information Network of the Inter-American Biodiversity Information Network, the Chinese Species Information System, and the Invasive Species Information Node of the US National Biological Information Infrastructure. We conclude that IAS networks require a common goal, dedicated leaders, effective communication, and broad endorsement, in order to obtain sustainable, long-term funding and long-term stability. They need to start small, use the experience of other networks, partner with others, and showcase benefits. Global integration and synergy among invasive species networks will succeed with contributions from both the top-down and the bottom-up. ?? 2006 Springer.

  10. Exploring the Combination of Dempster-Shafer Theory and Neural Network for Predicting Trust and Distrust

    PubMed Central

    Wang, Xin; Wang, Ying; Sun, Hongbin

    2016-01-01

    In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among inducing factors may be dependency, independence, and conflicting. Dempster-Shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. In this paper, we study trust and distrust prediction based on the combination of Dempster-Shafer theory and neural network. We firstly analyze the inducing factors about trust and distrust, namely, homophily, status theory, and emotion tendency. Then, we quantify inducing factors of trust and distrust, take these features as evidences, and construct evidence prototype as input nodes of multilayer neural network. Finally, we propose a framework of predicting trust and distrust which uses multilayer neural network to model the implementing process of Dempster-Shafer theory in different hidden layers, aiming to overcome the disadvantage of Dempster-Shafer theory without optimization method. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework. PMID:27034651

  11. Exploring the Combination of Dempster-Shafer Theory and Neural Network for Predicting Trust and Distrust.

    PubMed

    Wang, Xin; Wang, Ying; Sun, Hongbin

    2016-01-01

    In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among inducing factors may be dependency, independence, and conflicting. Dempster-Shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. In this paper, we study trust and distrust prediction based on the combination of Dempster-Shafer theory and neural network. We firstly analyze the inducing factors about trust and distrust, namely, homophily, status theory, and emotion tendency. Then, we quantify inducing factors of trust and distrust, take these features as evidences, and construct evidence prototype as input nodes of multilayer neural network. Finally, we propose a framework of predicting trust and distrust which uses multilayer neural network to model the implementing process of Dempster-Shafer theory in different hidden layers, aiming to overcome the disadvantage of Dempster-Shafer theory without optimization method. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework.

  12. Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

    USGS Publications Warehouse

    Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

    2000-01-01

    Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

  13. Chemical reaction network approaches to Biochemical Systems Theory.

    PubMed

    Arceo, Carlene Perpetua P; Jose, Editha C; Marin-Sanguino, Alberto; Mendoza, Eduardo R

    2015-11-01

    This paper provides a framework to represent a Biochemical Systems Theory (BST) model (in either GMA or S-system form) as a chemical reaction network with power law kinetics. Using this representation, some basic properties and the application of recent results of Chemical Reaction Network Theory regarding steady states of such systems are shown. In particular, Injectivity Theory, including network concordance [36] and the Jacobian Determinant Criterion [43], a "Lifting Theorem" for steady states [26] and the comprehensive results of Müller and Regensburger [31] on complex balanced equilibria are discussed. A partial extension of a recent Emulation Theorem of Cardelli for mass action systems [3] is derived for a subclass of power law kinetic systems. However, it is also shown that the GMA and S-system models of human purine metabolism [10] do not display the reactant-determined kinetics assumed by Müller and Regensburger and hence only a subset of BST models can be handled with their approach. Moreover, since the reaction networks underlying many BST models are not weakly reversible, results for non-complex balanced equilibria are also needed.

  14. Lewis Information Network (LINK): Background and overview

    NASA Technical Reports Server (NTRS)

    Schulte, Roger R.

    1987-01-01

    The NASA Lewis Research Center supports many research facilities with many isolated buildings, including wind tunnels, test cells, and research laboratories. These facilities are all located on a 350 acre campus adjacent to the Cleveland Hopkins Airport. The function of NASA-Lewis is to do basic and applied research in all areas of aeronautics, fluid mechanics, materials and structures, space propulsion, and energy systems. These functions require a great variety of remote high speed, high volume data communications for computing and interactive graphic capabilities. In addition, new requirements for local distribution of intercenter video teleconferencing and data communications via satellite have developed. To address these and future communications requirements for the next 15 yrs, a project team was organized to design and implement a new high speed communication system that would handle both data and video information in a common lab-wide Local Area Network. The project team selected cable television broadband coaxial cable technology as the communications medium and first installation of in-ground cable began in the summer of 1980. The Lewis Information Network (LINK) became operational in August 1982 and has become the backbone of all data communications and video.

  15. Reservoir computing: a photonic neural network for information processing

    NASA Astrophysics Data System (ADS)

    Paquot, Yvan; Dambre, Joni; Schrauwen, Benjamin; Haelterman, Marc; Massar, Serge

    2010-06-01

    At the boundaries between photonics and dynamic systems theory, we combine recent advances in neural networks with opto-electronic nonlinearities to demonstrate a new way to perform optical information processing. The concept of reservoir computing arose recently as a powerful solution to the issue of training recurrent neural networks. Indeed, it is comparable to, or even outperforms, other state of the art solutions for tasks such as speech recognition or time series prediction. As it is based on a static topology, it allows making the most of very simple physical architectures having complex nonlinear dynamics. The method is inherently robust to noise and does not require explicit programming operations. It is therefore particularly well adapted for analog realizations. Among the various implementations of the concept that have been proposed, we focus on the field of optics. Our experimental reservoir computer is based on opto-electronic technology, and can be viewed as an intermediate step towards an all optical device. Our fiber optics system is based on a nonlinear feedback loop operating at the threshold of chaos. In its present preliminary stage it is already capable of complicated tasks like modeling nonlinear systems with memory. Our aim is to demonstrate that such an analog reservoir can have performances comparable to state of the art digital implementations of Neural Networks. Furthermore, our system can in principle be operated at very high frequencies thanks to the high speed of photonic devices. Thus one could envisage targeting applications such as online information processing in broadband telecommunications.

  16. Nonlinear effective-medium theory of disordered spring networks.

    PubMed

    Sheinman, M; Broedersz, C P; MacKintosh, F C

    2012-02-01

    Disordered soft materials, such as fibrous networks in biological contexts, exhibit a nonlinear elastic response. We study such nonlinear behavior with a minimal model for networks on lattice geometries with simple Hookian elements with disordered spring constant. By developing a mean-field approach to calculate the differential elastic bulk modulus for the macroscopic network response of such networks under large isotropic deformations, we provide insight into the origins of the strain stiffening and softening behavior of these systems. We find that the nonlinear mechanics depends only weakly on the lattice geometry and is governed by the average network connectivity. In particular, the nonlinear response is controlled by the isostatic connectivity, which depends strongly on the applied strain. Our predictions for the strain dependence of the isostatic point as well as the strain-dependent differential bulk modulus agree well with numerical results in both two and three dimensions. In addition, by using a mapping between the disordered network and a regular network with random forces, we calculate the nonaffine fluctuations of the deformation field and compare them to the numerical results. Finally, we discuss the limitations and implications of the developed theory.

  17. Nonlinear effective-medium theory of disordered spring networks.

    PubMed

    Sheinman, M; Broedersz, C P; MacKintosh, F C

    2012-02-01

    Disordered soft materials, such as fibrous networks in biological contexts, exhibit a nonlinear elastic response. We study such nonlinear behavior with a minimal model for networks on lattice geometries with simple Hookian elements with disordered spring constant. By developing a mean-field approach to calculate the differential elastic bulk modulus for the macroscopic network response of such networks under large isotropic deformations, we provide insight into the origins of the strain stiffening and softening behavior of these systems. We find that the nonlinear mechanics depends only weakly on the lattice geometry and is governed by the average network connectivity. In particular, the nonlinear response is controlled by the isostatic connectivity, which depends strongly on the applied strain. Our predictions for the strain dependence of the isostatic point as well as the strain-dependent differential bulk modulus agree well with numerical results in both two and three dimensions. In addition, by using a mapping between the disordered network and a regular network with random forces, we calculate the nonaffine fluctuations of the deformation field and compare them to the numerical results. Finally, we discuss the limitations and implications of the developed theory. PMID:22463230

  18. Information spread in networks: Games, optimal control, and stabilization

    NASA Astrophysics Data System (ADS)

    Khanafer, Ali

    This thesis focuses on designing efficient mechanisms for controlling information spread in networks. We consider two models for information spread. The first one is the well-known distributed averaging dynamics. The second model is a nonlinear one that describes virus spread in computer and biological networks. We seek to design optimal, robust, and stabilizing controllers under practical constraints. For distributed averaging networks, we study the interaction between a network designer and an adversary. We consider two types of attacks on the network. In Attack-I, the adversary strategically disconnects a set of links to prevent the nodes from reaching consensus. Meanwhile, the network designer assists the nodes in reaching consensus by changing the weights of a limited number of links in the network. We formulate two problems to describe this competition where the order in which the players act is reversed in the two problems. Although the canonical equations provided by the Pontryagin's Maximum Principle (MP) seem to be intractable, we provide an alternative characterization for the optimal strategies that makes connection to potential theory. Further, we provide a sufficient condition for the existence of a saddle-point equilibrium (SPE) for the underlying zero-sum game. In Attack-II, the designer and the adversary are both capable of altering the measurements of all nodes in the network by injecting global signals. We impose two constraints on both players: a power constraint and an energy constraint. We assume that the available energy to each player is not sufficient to operate at maximum power throughout the horizon of the game. We show the existence of an SPE and derive the optimal strategies in closed form for this attack scenario. As an alternative to the "network designer vs. adversary" framework, we investigate the possibility of stabilizing unknown network diffusion processes using a distributed mechanism, where the uncertainty is due to an attack

  19. Modeling and dynamical topology properties of VANET based on complex networks theory

    SciTech Connect

    Zhang, Hong; Li, Jie

    2015-01-15

    Vehicular Ad hoc Network (VANET) is a special subset of multi-hop Mobile Ad hoc Networks in which vehicles can not only communicate with each other but also with the fixed equipments along the roads through wireless interfaces. Recently, it has been discovered that essential systems in real world share similar properties. When they are regarded as networks, among which the dynamic topology structure of VANET system is an important issue. Many real world networks are actually growing with preferential attachment like Internet, transportation system and telephone network. Those phenomena have brought great possibility in finding a strategy to calibrate and control the topology parameters which can help find VANET topology change regulation to relieve traffic jam, prevent traffic accident and improve traffic safety. VANET is a typical complex network which has its basic characteristics. In this paper, we focus on the macroscopic Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) inter-vehicle communication network with complex network theory. In particular, this paper is the first one to propose a method analyzing the topological structure and performance of VANET and present the communications in VANET from a new perspective. Accordingly, we propose degree distribution, clustering coefficient and the short path length of complex network to implement our strategy by numerical example and simulation. All the results demonstrate that VANET shows small world network features and is characterized by a truncated scale-free degree distribution with power-law degree distribution. The average path length of the network is simulated numerically, which indicates that the network shows small-world property and is rarely affected by the randomness. What’s more, we carry out extensive simulations of information propagation and mathematically prove the power law property when γ > 2. The results of this study provide useful information for VANET optimization from a

  20. Modeling and dynamical topology properties of VANET based on complex networks theory

    NASA Astrophysics Data System (ADS)

    Zhang, Hong; Li, Jie

    2015-01-01

    Vehicular Ad hoc Network (VANET) is a special subset of multi-hop Mobile Ad hoc Networks in which vehicles can not only communicate with each other but also with the fixed equipments along the roads through wireless interfaces. Recently, it has been discovered that essential systems in real world share similar properties. When they are regarded as networks, among which the dynamic topology structure of VANET system is an important issue. Many real world networks are actually growing with preferential attachment like Internet, transportation system and telephone network. Those phenomena have brought great possibility in finding a strategy to calibrate and control the topology parameters which can help find VANET topology change regulation to relieve traffic jam, prevent traffic accident and improve traffic safety. VANET is a typical complex network which has its basic characteristics. In this paper, we focus on the macroscopic Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) inter-vehicle communication network with complex network theory. In particular, this paper is the first one to propose a method analyzing the topological structure and performance of VANET and present the communications in VANET from a new perspective. Accordingly, we propose degree distribution, clustering coefficient and the short path length of complex network to implement our strategy by numerical example and simulation. All the results demonstrate that VANET shows small world network features and is characterized by a truncated scale-free degree distribution with power-law degree distribution. The average path length of the network is simulated numerically, which indicates that the network shows small-world property and is rarely affected by the randomness. What's more, we carry out extensive simulations of information propagation and mathematically prove the power law property when γ > 2. The results of this study provide useful information for VANET optimization from a

  1. Information processing in generalized probabilistic theories

    SciTech Connect

    Barrett, Jonathan

    2007-03-15

    I introduce a framework in which a variety of probabilistic theories can be defined, including classical and quantum theories, and many others. From two simple assumptions, a tensor product rule for combining separate systems can be derived. Certain features, usually thought of as specifically quantum, turn out to be generic in this framework, meaning that they are present in all except classical theories. These include the nonunique decomposition of a mixed state into pure states, a theorem involving disturbance of a system on measurement (suggesting that the possibility of secure key distribution is generic), and a no-cloning theorem. Two particular theories are then investigated in detail, for the sake of comparison with the classical and quantum cases. One of these includes states that can give rise to arbitrary nonsignaling correlations, including the superquantum correlations that have become known in the literature as nonlocal machines or Popescu-Rohrlich boxes. By investigating these correlations in the context of a theory with well-defined dynamics, I hope to make further progress with a question raised by Popescu and Rohrlich, which is why does quantum theory not allow these strongly nonlocal correlations? The existence of such correlations forces much of the dynamics in this theory to be, in a certain sense, classical, with consequences for teleportation, cryptography, and computation. I also investigate another theory in which all states are local. Finally, I raise the question of what further axiom(s) could be added to the framework in order to identify quantum theory uniquely, and hypothesize that quantum theory is optimal for computation.

  2. USING INFORMATION THEORY TO DEFINE A SUSTAINABILITY INDEX

    EPA Science Inventory

    Information theory has many applications in Ecology and Environmental science, such as a biodiversity indicator, as a measure of evolution, a measure of distance from thermodynamic equilibrium, and as a measure of system organization. Fisher Information, in particular, provides a...

  3. Identifying influential nodes in weighted networks based on evidence theory

    NASA Astrophysics Data System (ADS)

    Wei, Daijun; Deng, Xinyang; Zhang, Xiaoge; Deng, Yong; Mahadevan, Sankaran

    2013-05-01

    The design of an effective ranking method to identify influential nodes is an important problem in the study of complex networks. In this paper, a new centrality measure is proposed based on the Dempster-Shafer evidence theory. The proposed measure trades off between the degree and strength of every node in a weighted network. The influences of both the degree and the strength of each node are represented by basic probability assignment (BPA). The proposed centrality measure is determined by the combination of these BPAs. Numerical examples are used to illustrate the effectiveness of the proposed method.

  4. Empirical Laws and Theories of Information and Software Sciences.

    ERIC Educational Resources Information Center

    Zunde, Pranas

    1984-01-01

    Reviews what information and software sciences have thus far accomplished in search for empirical regularities and laws and examines what theories have been developed to explain and account for regularities and laws. Specific laws and theories of information highlighted are those of Zipf, Bradford, Lotka, Mandelbrot, and Simon. (Forty references)…

  5. VIOLIN: vaccine investigation and online information network.

    PubMed

    Xiang, Zuoshuang; Todd, Thomas; Ku, Kim P; Kovacic, Bethany L; Larson, Charles B; Chen, Fang; Hodges, Andrew P; Tian, Yuying; Olenzek, Elizabeth A; Zhao, Boyang; Colby, Lesley A; Rush, Howard G; Gilsdorf, Janet R; Jourdian, George W; He, Yongqun

    2008-01-01

    Vaccines are among the most efficacious and cost-effective tools for reducing morbidity and mortality caused by infectious diseases. The vaccine investigation and online information network (VIOLIN) is a web-based central resource, allowing easy curation, comparison and analysis of vaccine-related research data across various human pathogens (e.g. Haemophilus influenzae, human immunodeficiency virus (HIV) and Plasmodium falciparum) of medical importance and across humans, other natural hosts and laboratory animals. Vaccine-related peer-reviewed literature data have been downloaded into the database from PubMed and are searchable through various literature search programs. Vaccine data are also annotated, edited and submitted to the database through a web-based interactive system that integrates efficient computational literature mining and accurate manual curation. Curated information includes general microbial pathogenesis and host protective immunity, vaccine preparation and characteristics, stimulated host responses after vaccination and protection efficacy after challenge. Vaccine-related pathogen and host genes are also annotated and available for searching through customized BLAST programs. All VIOLIN data are available for download in an eXtensible Markup Language (XML)-based data exchange format. VIOLIN is expected to become a centralized source of vaccine information and to provide investigators in basic and clinical sciences with curated data and bioinformatics tools for vaccine research and development. VIOLIN is publicly available at http://www.violinet.org. PMID:18025042

  6. VIOLIN: vaccine investigation and online information network

    PubMed Central

    Xiang, Zuoshuang; Todd, Thomas; Ku, Kim P.; Kovacic, Bethany L.; Larson, Charles B.; Chen, Fang; Hodges, Andrew P.; Tian, Yuying; Olenzek, Elizabeth A.; Zhao, Boyang; Colby, Lesley A.; Rush, Howard G.; Gilsdorf, Janet R.; Jourdian, George W.; He, Yongqun

    2008-01-01

    Vaccines are among the most efficacious and cost-effective tools for reducing morbidity and mortality caused by infectious diseases. The vaccine investigation and online information network (VIOLIN) is a web-based central resource, allowing easy curation, comparison and analysis of vaccine-related research data across various human pathogens (e.g. Haemophilus influenzae, human immunodeficiency virus (HIV) and Plasmodium falciparum) of medical importance and across humans, other natural hosts and laboratory animals. Vaccine-related peer-reviewed literature data have been downloaded into the database from PubMed and are searchable through various literature search programs. Vaccine data are also annotated, edited and submitted to the database through a web-based interactive system that integrates efficient computational literature mining and accurate manual curation. Curated information includes general microbial pathogenesis and host protective immunity, vaccine preparation and characteristics, stimulated host responses after vaccination and protection efficacy after challenge. Vaccine-related pathogen and host genes are also annotated and available for searching through customized BLAST programs. All VIOLIN data are available for download in an eXtensible Markup Language (XML)-based data exchange format. VIOLIN is expected to become a centralized source of vaccine information and to provide investigators in basic and clinical sciences with curated data and bioinformatics tools for vaccine research and development. VIOLIN is publicly available at http://www.violinet.org PMID:18025042

  7. Investigating accident causation through information network modelling.

    PubMed

    Griffin, T G C; Young, M S; Stanton, N A

    2010-02-01

    Management of risk in complex domains such as aviation relies heavily on post-event investigations, requiring complex approaches to fully understand the integration of multi-causal, multi-agent and multi-linear accident sequences. The Event Analysis of Systemic Teamwork methodology (EAST; Stanton et al. 2008) offers such an approach based on network models. In this paper, we apply EAST to a well-known aviation accident case study, highlighting communication between agents as a central theme and investigating the potential for finding agents who were key to the accident. Ultimately, this work aims to develop a new model based on distributed situation awareness (DSA) to demonstrate that the risk inherent in a complex system is dependent on the information flowing within it. By identifying key agents and information elements, we can propose proactive design strategies to optimize the flow of information and help work towards avoiding aviation accidents. Statement of Relevance: This paper introduces a novel application of an holistic methodology for understanding aviation accidents. Furthermore, it introduces an ongoing project developing a nonlinear and prospective method that centralises distributed situation awareness and communication as themes. The relevance of findings are discussed in the context of current ergonomic and aviation issues of design, training and human-system interaction. PMID:20099174

  8. VIOLIN: vaccine investigation and online information network.

    PubMed

    Xiang, Zuoshuang; Todd, Thomas; Ku, Kim P; Kovacic, Bethany L; Larson, Charles B; Chen, Fang; Hodges, Andrew P; Tian, Yuying; Olenzek, Elizabeth A; Zhao, Boyang; Colby, Lesley A; Rush, Howard G; Gilsdorf, Janet R; Jourdian, George W; He, Yongqun

    2008-01-01

    Vaccines are among the most efficacious and cost-effective tools for reducing morbidity and mortality caused by infectious diseases. The vaccine investigation and online information network (VIOLIN) is a web-based central resource, allowing easy curation, comparison and analysis of vaccine-related research data across various human pathogens (e.g. Haemophilus influenzae, human immunodeficiency virus (HIV) and Plasmodium falciparum) of medical importance and across humans, other natural hosts and laboratory animals. Vaccine-related peer-reviewed literature data have been downloaded into the database from PubMed and are searchable through various literature search programs. Vaccine data are also annotated, edited and submitted to the database through a web-based interactive system that integrates efficient computational literature mining and accurate manual curation. Curated information includes general microbial pathogenesis and host protective immunity, vaccine preparation and characteristics, stimulated host responses after vaccination and protection efficacy after challenge. Vaccine-related pathogen and host genes are also annotated and available for searching through customized BLAST programs. All VIOLIN data are available for download in an eXtensible Markup Language (XML)-based data exchange format. VIOLIN is expected to become a centralized source of vaccine information and to provide investigators in basic and clinical sciences with curated data and bioinformatics tools for vaccine research and development. VIOLIN is publicly available at http://www.violinet.org.

  9. Deep Space Network information system architecture study

    NASA Technical Reports Server (NTRS)

    Beswick, C. A.; Markley, R. W. (Editor); Atkinson, D. J.; Cooper, L. P.; Tausworthe, R. C.; Masline, R. C.; Jenkins, J. S.; Crowe, R. A.; Thomas, J. L.; Stoloff, M. J.

    1992-01-01

    The purpose of this article is to describe an architecture for the DSN information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990's. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies--i.e., computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control.

  10. Training Records And Information Network UNIX Version

    SciTech Connect

    Johnston, Michael

    1996-12-01

    TRAIN-UNIX is used to track training requirements, qualifications, training completion and schedule training, classrooms and instructors. TRAIN-UNIX is a requirements-based system. When the identified training requirements for specific jobs are entered into the system, the employees manager or responsible training person assigns jobs to an employee. TRAIN-UNIX will then assemble an Individual Training Plan (ITP) with all courses required. ITP''s can also be modified to add any special training directed or identified by management, best business practices, procedures, etc. TRAIN-UNIX also schedules and tracks conferences, seminars, and required reading. TRAIN-UNIX is a secure database system on a server accessible via the network. Access to the user functions (scheduling, data entry, ITP modification etc.) within TRAIN-UNIX are granted by function, as needed, by the system administrator. An additional level of security allows those who access TRAIN-UNIX to only add, modify or view information for the organizations to which they belong. TRAIN-UNIX scheduling function allows network access to scheduling of students. As a function of the scheduling process, TRAIN-UNIX checks to insure that the student is a valid employee, not double booked, and the instructor and classroom are not double booked. TRAIN-UNIX will report pending lapse of courses or qualifications. This ability to know the lapse of training along with built in training requesting function allows the training deliverers to forecast training needs.

  11. Training Records And Information Network UNIX Version

    1996-12-01

    TRAIN-UNIX is used to track training requirements, qualifications, training completion and schedule training, classrooms and instructors. TRAIN-UNIX is a requirements-based system. When the identified training requirements for specific jobs are entered into the system, the employees manager or responsible training person assigns jobs to an employee. TRAIN-UNIX will then assemble an Individual Training Plan (ITP) with all courses required. ITP''s can also be modified to add any special training directed or identified by management, bestmore » business practices, procedures, etc. TRAIN-UNIX also schedules and tracks conferences, seminars, and required reading. TRAIN-UNIX is a secure database system on a server accessible via the network. Access to the user functions (scheduling, data entry, ITP modification etc.) within TRAIN-UNIX are granted by function, as needed, by the system administrator. An additional level of security allows those who access TRAIN-UNIX to only add, modify or view information for the organizations to which they belong. TRAIN-UNIX scheduling function allows network access to scheduling of students. As a function of the scheduling process, TRAIN-UNIX checks to insure that the student is a valid employee, not double booked, and the instructor and classroom are not double booked. TRAIN-UNIX will report pending lapse of courses or qualifications. This ability to know the lapse of training along with built in training requesting function allows the training deliverers to forecast training needs.« less

  12. An information-theoretic model for link prediction in complex networks

    PubMed Central

    Zhu, Boyao; Xia, Yongxiang

    2015-01-01

    Various structural features of networks have been applied to develop link prediction methods. However, because different features highlight different aspects of network structural properties, it is very difficult to benefit from all of the features that might be available. In this paper, we investigate the role of network topology in predicting missing links from the perspective of information theory. In this way, the contributions of different structural features to link prediction are measured in terms of their values of information. Then, an information-theoretic model is proposed that is applicable to multiple structural features. Furthermore, we design a novel link prediction index, called Neighbor Set Information (NSI), based on the information-theoretic model. According to our experimental results, the NSI index performs well in real-world networks, compared with other typical proximity indices. PMID:26335758

  13. Business information query expansion through semantic network

    NASA Astrophysics Data System (ADS)

    Gong, Zhiguo; Muyeba, Maybin; Guo, Jingzhi

    2010-02-01

    In this article, we propose a method for business information query expansions. In our approach, hypernym/hyponymy and synonym relations in WordNet are used as the basic expansion rules. Then we use WordNet Lexical Chains and WordNet semantic similarity to assign terms in the same query into different groups with respect to their semantic similarities. For each group, we expand the highest terms in the WordNet hierarchies with hypernym and synonym, the lowest terms with hyponym and synonym and all other terms with only synonym. In this way, the contradictory caused by full expansion can be well controlled. Furthermore, we use collection-related term semantic network to further improve the expansion performance. And our experiment reveals that our solution for query expansion can improve the query performance dramatically.

  14. Bayesian information fusion networks for biosurveillance applications.

    PubMed

    Mnatsakanyan, Zaruhi R; Burkom, Howard S; Coberly, Jacqueline S; Lombardo, Joseph S

    2009-01-01

    This study introduces new information fusion algorithms to enhance disease surveillance systems with Bayesian decision support capabilities. A detection system was built and tested using chief complaints from emergency department visits, International Classification of Diseases Revision 9 (ICD-9) codes from records of outpatient visits to civilian and military facilities, and influenza surveillance data from health departments in the National Capital Region (NCR). Data anomalies were identified and distribution of time offsets between events in the multiple data streams were established. The Bayesian Network was built to fuse data from multiple sources and identify influenza-like epidemiologically relevant events. Results showed increased specificity compared with the alerts generated by temporal anomaly detection algorithms currently deployed by NCR health departments. Further research should be done to investigate correlations between data sources for efficient fusion of the collected data.

  15. Improving clustering by imposing network information

    PubMed Central

    Gerber, Susanne; Horenko, Illia

    2015-01-01

    Cluster analysis is one of the most popular data analysis tools in a wide range of applied disciplines. We propose and justify a computationally efficient and straightforward-to-implement way of imposing the available information from networks/graphs (a priori available in many application areas) on a broad family of clustering methods. The introduced approach is illustrated on the problem of a noninvasive unsupervised brain signal classification. This task is faced with several challenging difficulties such as nonstationary noisy signals and a small sample size, combined with a high-dimensional feature space and huge noise-to-signal ratios. Applying this approach results in an exact unsupervised classification of very short signals, opening new possibilities for clustering methods in the area of a noninvasive brain-computer interface. PMID:26601225

  16. Integrated condition monitoring of space information network

    NASA Astrophysics Data System (ADS)

    Wang, Zhilin; Li, Xinming; Li, Yachen; Yu, Shaolin

    2015-11-01

    In order to solve the integrated condition monitoring problem in space information network, there are three works finished including analyzing the characteristics of tasks process and system health monitoring, adopting the automata modeling method, and respectively establishing the models for state inference and state determination. The state inference model is a logic automaton and is gotten by concluding engineering experiences. The state determination model is a double-layer automaton, the lower automaton is responsible for parameter judge and the upper automaton is responsible for state diagnosis. At last, the system state monitoring algorithm has been proposed, which realizes the integrated condition monitoring for task process and system health, and can avoid the false alarm.

  17. Modelling mechanical characteristics of microbial biofilms by network theory

    PubMed Central

    Ehret, Alexander E.; Böl, Markus

    2013-01-01

    In this contribution, we present a constitutive model to describe the mechanical behaviour of microbial biofilms based on classical approaches in the continuum theory of polymer networks. Although the model is particularly developed for the well-studied biofilms formed by mucoid Pseudomonas aeruginosa strains, it could easily be adapted to other biofilms. The basic assumption behind the model is that the network of extracellular polymeric substances can be described as a superposition of worm-like chain networks, each connected by transient junctions of a certain lifetime. Several models that were applied to biofilms previously are included in the presented approach as special cases, and for small shear strains, the governing equations are those of four parallel Maxwell elements. Rheological data given in the literature are very adequately captured by the proposed model, and the simulated response for a series of compression tests at large strains is in good qualitative agreement with reported experimental behaviour. PMID:23034354

  18. Implications of information theory in optical fibre communications.

    PubMed

    Agrell, Erik; Alvarado, Alex; Kschischang, Frank R

    2016-03-01

    Recent decades have witnessed steady improvements in our ability to harness the information-carrying capability of optical fibres. Will this process continue, or will progress eventually stall? Information theory predicts that all channels have a limited capacity depending on the available transmission resources, and thus it is inevitable that the pace of improvements will slow. However, information theory also provides insights into how transmission resources should, in principle, best be exploited, and thus may serve as a guide for where to look for better ways to squeeze more out of a precious resource. This tutorial paper reviews the basic concepts of information theory and their application in fibre-optic communications.

  19. Actor-network theory: a tool to support ethical analysis of commercial genetic testing.

    PubMed

    Williams-Jones, Bryn; Graham, Janice E

    2003-12-01

    Social, ethical and policy analysis of the issues arising from gene patenting and commercial genetic testing is enhanced by the application of science and technology studies, and Actor-Network Theory (ANT) in particular. We suggest the potential for transferring ANT's flexible nature to an applied heuristic methodology for gathering empirical information and for analysing the complex networks involved in the development of genetic technologies. Three concepts are explored in this paper--actor-networks, translation, and drift--and applied to the case of Myriad Genetics and their commercial BRACAnalysis genetic susceptibility test for hereditary breast cancer. Treating this test as an active participant in socio-technical networks clarifies the extent to which it interacts with, shapes and is shaped by people, other technologies, and institutions. Such an understanding enables more sophisticated and nuanced technology assessment, academic analysis, as well as public debate about the social, ethical and policy implications of the commercialization of new genetic technologies. PMID:15115034

  20. Strengthening Prevention Program Theories and Evaluations: Contributions from Social Network Analysis

    PubMed Central

    Gest, Scott D.; Osgood, D. Wayne; Feinberg, Mark; Bierman, Karen L.; Moody, James

    2011-01-01

    A majority of school-based prevention programs target the modification of setting-level social dynamics, either explicitly (e.g., by changing schools’ organizational, cultural or instructional systems that influence children’s relationships), or implicitly (e.g., by altering behavioral norms designed to influence children’s social affiliations and interactions). Yet, in outcome analyses of these programs, the rich and complicated set of peer network dynamics is often reduced to an aggregation of individual characteristics or assessed with methods that do not account for the interdependencies of network data. In this paper, we present concepts and analytic methods from the field of social network analysis and illustrate their great value to prevention science – both as a source of tools for refining program theories and as methods that enable more sophisticated and focused tests of intervention effects. An additional goal is to inform discussions of the broader implications of social network analysis for public health efforts. PMID:21728069

  1. Complex Network Theory Applied to the Growth of Kuala Lumpur's Public Urban Rail Transit Network.

    PubMed

    Ding, Rui; Ujang, Norsidah; Hamid, Hussain Bin; Wu, Jianjun

    2015-01-01

    Recently, the number of studies involving complex network applications in transportation has increased steadily as scholars from various fields analyze traffic networks. Nonetheless, research on rail network growth is relatively rare. This research examines the evolution of the Public Urban Rail Transit Networks of Kuala Lumpur (PURTNoKL) based on complex network theory and covers both the topological structure of the rail system and future trends in network growth. In addition, network performance when facing different attack strategies is also assessed. Three topological network characteristics are considered: connections, clustering and centrality. In PURTNoKL, we found that the total number of nodes and edges exhibit a linear relationship and that the average degree stays within the interval [2.0488, 2.6774] with heavy-tailed distributions. The evolutionary process shows that the cumulative probability distribution (CPD) of degree and the average shortest path length show good fit with exponential distribution and normal distribution, respectively. Moreover, PURTNoKL exhibits clear cluster characteristics; most of the nodes have a 2-core value, and the CPDs of the centrality's closeness and betweenness follow a normal distribution function and an exponential distribution, respectively. Finally, we discuss four different types of network growth styles and the line extension process, which reveal that the rail network's growth is likely based on the nodes with the biggest lengths of the shortest path and that network protection should emphasize those nodes with the largest degrees and the highest betweenness values. This research may enhance the networkability of the rail system and better shape the future growth of public rail networks. PMID:26448645

  2. Complex Network Theory Applied to the Growth of Kuala Lumpur's Public Urban Rail Transit Network.

    PubMed

    Ding, Rui; Ujang, Norsidah; Hamid, Hussain Bin; Wu, Jianjun

    2015-01-01

    Recently, the number of studies involving complex network applications in transportation has increased steadily as scholars from various fields analyze traffic networks. Nonetheless, research on rail network growth is relatively rare. This research examines the evolution of the Public Urban Rail Transit Networks of Kuala Lumpur (PURTNoKL) based on complex network theory and covers both the topological structure of the rail system and future trends in network growth. In addition, network performance when facing different attack strategies is also assessed. Three topological network characteristics are considered: connections, clustering and centrality. In PURTNoKL, we found that the total number of nodes and edges exhibit a linear relationship and that the average degree stays within the interval [2.0488, 2.6774] with heavy-tailed distributions. The evolutionary process shows that the cumulative probability distribution (CPD) of degree and the average shortest path length show good fit with exponential distribution and normal distribution, respectively. Moreover, PURTNoKL exhibits clear cluster characteristics; most of the nodes have a 2-core value, and the CPDs of the centrality's closeness and betweenness follow a normal distribution function and an exponential distribution, respectively. Finally, we discuss four different types of network growth styles and the line extension process, which reveal that the rail network's growth is likely based on the nodes with the biggest lengths of the shortest path and that network protection should emphasize those nodes with the largest degrees and the highest betweenness values. This research may enhance the networkability of the rail system and better shape the future growth of public rail networks.

  3. New scaling relation for information transfer in biological networks.

    PubMed

    Kim, Hyunju; Davies, Paul; Walker, Sara Imari

    2015-12-01

    We quantify characteristics of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast Schizosaccharomyces pombe (Davidich et al. 2008 PLoS ONE 3, e1672 (doi:10.1371/journal.pone.0001672)) and that of the budding yeast Saccharomyces cerevisiae (Li et al. 2004 Proc. Natl Acad. Sci. USA 101, 4781-4786 (doi:10.1073/pnas.0305937101)). We compare our results for these biological networks with the same analysis performed on ensembles of two different types of random networks: Erdös-Rényi and scale-free. We show that both biological networks share features in common that are not shared by either random network ensemble. In particular, the biological networks in our study process more information than the random networks on average. Both biological networks also exhibit a scaling relation in information transferred between nodes that distinguishes them from random, where the biological networks stand out as distinct even when compared with random networks that share important topological properties, such as degree distribution, with the biological network. We show that the most biologically distinct regime of this scaling relation is associated with a subset of control nodes that regulate the dynamics and function of each respective biological network. Information processing in biological networks is therefore interpreted as an emergent property of topology (causal structure) and dynamics (function). Our results demonstrate quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not share the same informational properties. PMID:26701883

  4. New scaling relation for information transfer in biological networks.

    PubMed

    Kim, Hyunju; Davies, Paul; Walker, Sara Imari

    2015-12-01

    We quantify characteristics of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast Schizosaccharomyces pombe (Davidich et al. 2008 PLoS ONE 3, e1672 (doi:10.1371/journal.pone.0001672)) and that of the budding yeast Saccharomyces cerevisiae (Li et al. 2004 Proc. Natl Acad. Sci. USA 101, 4781-4786 (doi:10.1073/pnas.0305937101)). We compare our results for these biological networks with the same analysis performed on ensembles of two different types of random networks: Erdös-Rényi and scale-free. We show that both biological networks share features in common that are not shared by either random network ensemble. In particular, the biological networks in our study process more information than the random networks on average. Both biological networks also exhibit a scaling relation in information transferred between nodes that distinguishes them from random, where the biological networks stand out as distinct even when compared with random networks that share important topological properties, such as degree distribution, with the biological network. We show that the most biologically distinct regime of this scaling relation is associated with a subset of control nodes that regulate the dynamics and function of each respective biological network. Information processing in biological networks is therefore interpreted as an emergent property of topology (causal structure) and dynamics (function). Our results demonstrate quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not share the same informational properties.

  5. Game Theory for Wireless Sensor Networks: A Survey

    PubMed Central

    Shi, Hai-Yan; Wang, Wan-Liang; Kwok, Ngai-Ming; Chen, Sheng-Yong

    2012-01-01

    Game theory (GT) is a mathematical method that describes the phenomenon of conflict and cooperation between intelligent rational decision-makers. In particular, the theory has been proven very useful in the design of wireless sensor networks (WSNs). This article surveys the recent developments and findings of GT, its applications in WSNs, and provides the community a general view of this vibrant research area. We first introduce the typical formulation of GT in the WSN application domain. The roles of GT are described that include routing protocol design, topology control, power control and energy saving, packet forwarding, data collection, spectrum allocation, bandwidth allocation, quality of service control, coverage optimization, WSN security, and other sensor management tasks. Then, three variations of game theory are described, namely, the cooperative, non-cooperative, and repeated schemes. Finally, existing problems and future trends are identified for researchers and engineers in the field. PMID:23012533

  6. Cross-talk theory of memory capacity in neural networks.

    PubMed

    MacGregor, R J; Gerstein, G L

    1991-01-01

    The present paper presents a theory for the mechanics of cross-talk among constituent neurons in networks in which multiple memory traces have been embedded, and develops criteria for memory capacity based on the disruptive influences of this cross-talk. The theory is based on interconnection patterns defined by the sequential configuration model of dynamic firing patterns. The theory accurately predicts the memory capacities observed in computer simulated nets, and predicts that cortical-like modules should be able to store up to about 300-900 selectively retrievable memory traces before disruption by cross-talk is likely. It also predicts that the cortex may has designed itself for modules of 30,000 neurons to at least in part to optimize memory capacity.

  7. Game theory for Wireless Sensor Networks: a survey.

    PubMed

    Shi, Hai-Yan; Wang, Wan-Liang; Kwok, Ngai-Ming; Chen, Sheng-Yong

    2012-01-01

    Game theory (GT) is a mathematical method that describes the phenomenon of conflict and cooperation between intelligent rational decision-makers. In particular, the theory has been proven very useful in the design of wireless sensor networks (WSNs). This article surveys the recent developments and findings of GT, its applications in WSNs, and provides the community a general view of this vibrant research area. We first introduce the typical formulation of GT in the WSN application domain. The roles of GT are described that include routing protocol design, topology control, power control and energy saving, packet forwarding, data collection, spectrum allocation, bandwidth allocation, quality of service control, coverage optimization, WSN security, and other sensor management tasks. Then, three variations of game theory are described, namely, the cooperative, non-cooperative, and repeated schemes. Finally, existing problems and future trends are identified for researchers and engineers in the field.

  8. Kinetic energy decomposition scheme based on information theory.

    PubMed

    Imamura, Yutaka; Suzuki, Jun; Nakai, Hiromi

    2013-12-15

    We proposed a novel kinetic energy decomposition analysis based on information theory. Since the Hirshfeld partitioning for electron densities can be formulated in terms of Kullback-Leibler information deficiency in information theory, a similar partitioning for kinetic energy densities was newly proposed. The numerical assessments confirm that the current kinetic energy decomposition scheme provides reasonable chemical pictures for ionic and covalent molecules, and can also estimate atomic energies using a correction with viral ratios.

  9. Information content and cross-talk in biological signal transduction: An information theory study

    NASA Astrophysics Data System (ADS)

    Prasad, Ashok; Lyons, Samanthe

    2014-03-01

    Biological cells respond to chemical cues provided by extra-cellular chemical signals, but many of these chemical signals and the pathways they activate interfere and overlap with one another. How well cells can distinguish between interfering extra-cellular signals is thus an important question in cellular signal transduction. Here we use information theory with stochastic simulations of networks to address the question of what happens to total information content when signals interfere. We find that both total information transmitted by the biological pathway, as well as its theoretical capacity to discriminate between overlapping signals, are relatively insensitive to cross-talk between the extracellular signals, until significantly high levels of cross-talk have been reached. This robustness of information content against cross-talk requires that the average amplitude of the signals are large. We predict that smaller systems, as exemplified by simple phosphorylation relays (two-component systems) in bacteria, should be significantly much less robust against cross-talk. Our results suggest that mammalian signal transduction can tolerate a high amount of cross-talk without degrading information content, while smaller bacterial systems cannot.

  10. The use of information theory in evolutionary biology.

    PubMed

    Adami, Christoph

    2012-05-01

    Information is a key concept in evolutionary biology. Information stored in a biological organism's genome is used to generate the organism and to maintain and control it. Information is also that which evolves. When a population adapts to a local environment, information about this environment is fixed in a representative genome. However, when an environment changes, information can be lost. At the same time, information is processed by animal brains to survive in complex environments, and the capacity for information processing also evolves. Here, I review applications of information theory to the evolution of proteins and to the evolution of information processing in simulated agents that adapt to perform a complex task.

  11. From information theory to quantitative description of steric effects.

    PubMed

    Alipour, Mojtaba; Safari, Zahra

    2016-07-21

    Immense efforts have been made in the literature to apply the information theory descriptors for investigating the electronic structure theory of various systems. In the present study, the information theoretic quantities, such as Fisher information, Shannon entropy, Onicescu information energy, and Ghosh-Berkowitz-Parr entropy, have been used to present a quantitative description for one of the most widely used concepts in chemistry, namely the steric effects. Taking the experimental steric scales for the different compounds as benchmark sets, there are reasonable linear relationships between the experimental scales of the steric effects and theoretical values of steric energies calculated from information theory functionals. Perusing the results obtained from the information theoretic quantities with the two representations of electron density and shape function, the Shannon entropy has the best performance for the purpose. On the one hand, the usefulness of considering the contributions of functional groups steric energies and geometries, and on the other hand, dissecting the effects of both global and local information measures simultaneously have also been explored. Furthermore, the utility of the information functionals for the description of steric effects in several chemical transformations, such as electrophilic and nucleophilic reactions and host-guest chemistry, has been analyzed. The functionals of information theory correlate remarkably with the stability of systems and experimental scales. Overall, these findings show that the information theoretic quantities can be introduced as quantitative measures of steric effects and provide further evidences of the quality of information theory toward helping theoreticians and experimentalists to interpret different problems in real systems.

  12. Analysis and improvement of vehicle information sharing networks

    NASA Astrophysics Data System (ADS)

    Gong, Hang; He, Kun; Qu, Yingchun; Wang, Pu

    2016-06-01

    Based on large-scale mobile phone data, mobility demand was estimated and locations of vehicles were inferred in the Boston area. Using the spatial distribution of vehicles, we analyze the vehicle information sharing network generated by the vehicle-to-vehicle (V2V) communications. Although a giant vehicle cluster is observed, the coverage and the efficiency of the information sharing network remain limited. Consequently, we propose a method to extend the information sharing network's coverage by adding long-range connections between targeted vehicle clusters. Furthermore, we employ the optimal design strategy discovered in square lattice to improve the efficiency of the vehicle information sharing network.

  13. Electronic Information and Applications in Musicology and Music Theory.

    ERIC Educational Resources Information Center

    Duggan, Mary Kay

    1992-01-01

    Describes electronic publishing and information resources in the field of music. Topics addressed include bibliographic citations of books, journal articles, scores, and sound recordings; bibliographic utilities; computer network resources; electronic music applications; tutorial and laboratory projects; interactive multimedia publications; and…

  14. A theory of information with special application to search problems.

    PubMed

    Wilbur, W J; Neuwald, A F

    2000-01-01

    Classical information theory concerns itself with communication through a noisy channel and how much one can infer about the channel input from a knowledge of the channel output. Because the channel is noisy the input and output are only related statistically and the rate of information transmission is a statistical concept with little meaning for the individual symbol used in transmission. Here we develop a more intuitive notion of information that is concerned with asking the right questions--that is, with finding those questions whose answer conveys the most information. We call this confirmatory information. In the first part of the paper we develop the general theory, show how it relates to classical information theory, and how in the special case of search problems it allows us to quantify the efficacy of information transmission regarding individual events. That is, confirmatory information measures how well a search for items having certain observable properties retrieves items having some unobserved property of interest. Thus confirmatory information facilitates a useful analysis of search problems and contrasts with classical information theory, which quantifies the efficiency of information transmission but is indifferent to the nature of the particular information being transmitted. The last part of the paper presents several examples where confirmatory information is used to quantify protein structural properties in a search setting. PMID:10642878

  15. How mirror-touch informs theories of synesthesia.

    PubMed

    Meier, Beat; Lunke, Katrin; Rothen, Nicolas

    2015-01-01

    Ward and Banissy provide an excellent overview of the state of mirror-touch research in order to advance this field. They present a comparison of two prominent theoretical approaches for understanding mirror-touch phenomena. According to the threshold theory, the phenomena arise as a result of a hyperactive mirror neuron system. According to the Self-Other Theory, they are due to disturbances in the ability to distinguish the self from others. Here, we explore how these two theories can inform theories of synesthesia more generally. We conclude that both theories are not suited as general models of synesthesia.

  16. How mirror-touch informs theories of synesthesia.

    PubMed

    Meier, Beat; Lunke, Katrin; Rothen, Nicolas

    2015-01-01

    Ward and Banissy provide an excellent overview of the state of mirror-touch research in order to advance this field. They present a comparison of two prominent theoretical approaches for understanding mirror-touch phenomena. According to the threshold theory, the phenomena arise as a result of a hyperactive mirror neuron system. According to the Self-Other Theory, they are due to disturbances in the ability to distinguish the self from others. Here, we explore how these two theories can inform theories of synesthesia more generally. We conclude that both theories are not suited as general models of synesthesia. PMID:26118388

  17. Information diversity in structure and dynamics of simulated neuronal networks.

    PubMed

    Mäki-Marttunen, Tuomo; Aćimović, Jugoslava; Nykter, Matti; Kesseli, Juha; Ruohonen, Keijo; Yli-Harja, Olli; Linne, Marja-Leena

    2011-01-01

    Neuronal networks exhibit a wide diversity of structures, which contributes to the diversity of the dynamics therein. The presented work applies an information theoretic framework to simultaneously analyze structure and dynamics in neuronal networks. Information diversity within the structure and dynamics of a neuronal network is studied using the normalized compression distance. To describe the structure, a scheme for generating distance-dependent networks with identical in-degree distribution but variable strength of dependence on distance is presented. The resulting network structure classes possess differing path length and clustering coefficient distributions. In parallel, comparable realistic neuronal networks are generated with NETMORPH simulator and similar analysis is done on them. To describe the dynamics, network spike trains are simulated using different network structures and their bursting behaviors are analyzed. For the simulation of the network activity the Izhikevich model of spiking neurons is used together with the Tsodyks model of dynamical synapses. We show that the structure of the simulated neuronal networks affects the spontaneous bursting activity when measured with bursting frequency and a set of intraburst measures: the more locally connected networks produce more and longer bursts than the more random networks. The information diversity of the structure of a network is greatest in the most locally connected networks, smallest in random networks, and somewhere in between in the networks between order and disorder. As for the dynamics, the most locally connected networks and some of the in-between networks produce the most complex intraburst spike trains. The same result also holds for sparser of the two considered network densities in the case of full spike trains.

  18. Can computational goals inform theories of vision?

    PubMed

    Anderson, Barton L

    2015-04-01

    One of the most lasting contributions of Marr's posthumous book is his articulation of the different "levels of analysis" that are needed to understand vision. Although a variety of work has examined how these different levels are related, there is comparatively little examination of the assumptions on which his proposed levels rest, or the plausibility of the approach Marr articulated given those assumptions. Marr placed particular significance on computational level theory, which specifies the "goal" of a computation, its appropriateness for solving a particular problem, and the logic by which it can be carried out. The structure of computational level theory is inherently teleological: What the brain does is described in terms of its purpose. I argue that computational level theory, and the reverse-engineering approach it inspires, requires understanding the historical trajectory that gave rise to functional capacities that can be meaningfully attributed with some sense of purpose or goal, that is, a reconstruction of the fitness function on which natural selection acted in shaping our visual abilities. I argue that this reconstruction is required to distinguish abilities shaped by natural selection-"natural tasks" -from evolutionary "by-products" (spandrels, co-optations, and exaptations), rather than merely demonstrating that computational goals can be embedded in a Bayesian model that renders a particular behavior or process rational. PMID:25772207

  19. Organizational Application of Social Networking Information Technologies

    ERIC Educational Resources Information Center

    Reppert, Jeffrey R.

    2012-01-01

    The focus of this qualitative research study using the Delphi method is to provide a framework for leaders to develop their own social networks. By exploring concerns in four areas, leaders may be able to better plan, implement, and manage social networking systems in organizations. The areas addressed are: (a) social networking using…

  20. A complex network model for seismicity based on mutual information

    NASA Astrophysics Data System (ADS)

    Jiménez, Abigail

    2013-05-01

    Seismicity is the product of the interaction between the different parts of the lithosphere. Here, we model each part of the Earth as a cell that is constantly communicating its state to its environment. As a neuron is stimulated and produces an output, the different parts of the lithosphere are constantly stimulated by both other cells and the ductile part of the lithosphere, and produce an output in the form of a stress transfer or an earthquake. This output depends on the properties of each part of the Earth’s crust and the magnitude of the inputs. In this study, we propose an approach to the quantification of this communication, with the aid of the Information Theory, and model seismicity as a Complex Network. We have used data from California, and this new approach gives a better understanding of the processes involved in the formation of seismic patterns in that region.

  1. REGIME CHANGES IN ECOLOGICAL SYSTEMS: AN INFORMATION THEORY APPROACH

    EPA Science Inventory

    We present our efforts at developing an ecological system using Information Theory. We derive an expression for Fisher Information based on sampling of the system trajectory as it evolves in the state space. The Fisher Information index as we have derived it captures the characte...

  2. PREFACE: Complex Networks: from Biology to Information Technology

    NASA Astrophysics Data System (ADS)

    Barrat, A.; Boccaletti, S.; Caldarelli, G.; Chessa, A.; Latora, V.; Motter, A. E.

    2008-06-01

    The field of complex networks is one of the most active areas in contemporary statistical physics. Ten years after seminal work initiated the modern study of networks, interest in the field is in fact still growing, as indicated by the ever increasing number of publications in network science. The reason for such a resounding success is most likely the simplicity and broad significance of the approach that, through graph theory, allows researchers to address a variety of different complex systems within a common framework. This special issue comprises a selection of contributions presented at the workshop 'Complex Networks: from Biology to Information Technology' held in July 2007 in Pula (Cagliari), Italy as a satellite of the general conference STATPHYS23. The contributions cover a wide range of problems that are currently among the most important questions in the area of complex networks and that are likely to stimulate future research. The issue is organised into four sections. The first two sections describe 'methods' to study the structure and the dynamics of complex networks, respectively. After this methodological part, the issue proceeds with a section on applications to biological systems. The issue closes with a section concentrating on applications to the study of social and technological networks. The first section, entitled Methods: The Structure, consists of six contributions focused on the characterisation and analysis of structural properties of complex networks: The paper Motif-based communities in complex networks by Arenas et al is a study of the occurrence of characteristic small subgraphs in complex networks. These subgraphs, known as motifs, are used to define general classes of nodes and their communities by extending the mathematical expression of the Newman-Girvan modularity. The same line of research, aimed at characterising network structure through the analysis of particular subgraphs, is explored by Bianconi and Gulbahce in Algorithm

  3. Discovery of Information Diffusion Process in Social Networks

    NASA Astrophysics Data System (ADS)

    Kim, Kwanho; Jung, Jae-Yoon; Park, Jonghun

    Information diffusion analysis in social networks is of significance since it enables us to deeply understand dynamic social interactions among users. In this paper, we introduce approaches to discovering information diffusion process in social networks based on process mining. Process mining techniques are applied from three perspectives: social network analysis, process discovery and community recognition. We then present experimental results by using a real-life social network data. The proposed techniques are expected to employ as new analytical tools in online social networks such as blog and wikis for company marketers, politicians, news reporters and online writers.

  4. Dretske's Semantic Information Theory and Metatheories in Library and Information Science.

    ERIC Educational Resources Information Center

    Bonnevie, Ellen

    2001-01-01

    Presents the semantic information theory, formulated by the philosopher Fred Dretske, as a contribution to the discussion of metatheories and their practical implications in library and information science. Highlights include mathematical communication theory; digitization, perception, cognition, and concept formation; information and knowledge;…

  5. Extracting spatial information from networks with low-order eigenvectors

    NASA Astrophysics Data System (ADS)

    Cucuringu, Mihai; Blondel, Vincent D.; Van Dooren, Paul

    2013-03-01

    We consider the problem of inferring meaningful spatial information in networks from incomplete information on the connection intensity between the nodes of the network. We consider two spatially distributed networks: a population migration flow network within the US, and a network of mobile phone calls between cities in Belgium. For both networks we use the eigenvectors of the Laplacian matrix constructed from the link intensities to obtain informative visualizations and capture natural geographical subdivisions. We observe that some low-order eigenvectors localize very well and seem to reveal small geographically cohesive regions that match remarkably well with political and administrative boundaries. We discuss possible explanations for this observation by describing diffusion maps and localized eigenfunctions. In addition, we discuss a possible connection with the weighted graph cut problem, and provide numerical evidence supporting the idea that lower-order eigenvectors point out local cuts in the network. However, we do not provide a formal and rigorous justification for our observations.

  6. Query Networks for Medical Information Retrieval-Assigning Probabilistic Relationships

    PubMed Central

    Cousins, Steve B.; Silverstein, Jonathan C.; Frisse, Mark E.

    1990-01-01

    Query networks are specializations of Belief networks used in information retrieval. We hypothesize that query networks can be incorporated into medical information systems in at least two ways: First, the relative values of nodes in the query networks can be used to initiate searches based on query term-weights. Second, query models can incorporate reader feedback and can become simple task-specific user models. If large query networks are to be useful, one must find means to assign reasonable “default” values to those nodes and edges which are not explicitly defined by some other means. This paper presents preliminary data assessing the suitability of various default heuristic query network edge assignment functions. Early evidence suggests that query networks using default assignment functions exhibit behavior consistent with that expected from an information retrieval aid.

  7. Optimal multi-community network modularity for information diffusion

    NASA Astrophysics Data System (ADS)

    Wu, Jiaocan; Du, Ruping; Zheng, Yingying; Liu, Dong

    2016-02-01

    Studies demonstrate that community structure plays an important role in information spreading recently. In this paper, we investigate the impact of multi-community structure on information diffusion with linear threshold model. We utilize extended GN network that contains four communities and analyze dynamic behaviors of information that spreads on it. And we discover the optimal multi-community network modularity for information diffusion based on the social reinforcement. Results show that, within the appropriate range, multi-community structure will facilitate information diffusion instead of hindering it, which accords with the results derived from two-community network.

  8. Bridging disparate symptoms of schizophrenia: a triple network dysfunction theory.

    PubMed

    Nekovarova, Tereza; Fajnerova, Iveta; Horacek, Jiri; Spaniel, Filip

    2014-01-01

    Schizophrenia is a complex neuropsychiatric disorder with variable symptomatology, traditionally divided into positive and negative symptoms, and cognitive deficits. However, the etiology of this disorder has yet to be fully understood. Recent findings suggest that alteration of the basic sense of self-awareness may be an essential distortion of schizophrenia spectrum disorders. In addition, extensive research of social and mentalizing abilities has stressed the role of distortion of social skills in schizophrenia.This article aims to propose and support a concept of a triple brain network model of the dysfunctional switching between default mode and central executive network (CEN) related to the aberrant activity of the salience network. This model could represent a unitary mechanism of a wide array of symptom domains present in schizophrenia including the deficit of self (self-awareness and self-representation) and theory of mind (ToM) dysfunctions along with the traditional positive, negative and cognitive domains. We review previous studies which document the dysfunctions of self and ToM in schizophrenia together with neuroimaging data that support the triple brain network model as a common neuronal substrate of this dysfunction.

  9. Bridging disparate symptoms of schizophrenia: a triple network dysfunction theory

    PubMed Central

    Nekovarova, Tereza; Fajnerova, Iveta; Horacek, Jiri; Spaniel, Filip

    2014-01-01

    Schizophrenia is a complex neuropsychiatric disorder with variable symptomatology, traditionally divided into positive and negative symptoms, and cognitive deficits. However, the etiology of this disorder has yet to be fully understood. Recent findings suggest that alteration of the basic sense of self-awareness may be an essential distortion of schizophrenia spectrum disorders. In addition, extensive research of social and mentalizing abilities has stressed the role of distortion of social skills in schizophrenia.This article aims to propose and support a concept of a triple brain network model of the dysfunctional switching between default mode and central executive network (CEN) related to the aberrant activity of the salience network. This model could represent a unitary mechanism of a wide array of symptom domains present in schizophrenia including the deficit of self (self-awareness and self-representation) and theory of mind (ToM) dysfunctions along with the traditional positive, negative and cognitive domains. We review previous studies which document the dysfunctions of self and ToM in schizophrenia together with neuroimaging data that support the triple brain network model as a common neuronal substrate of this dysfunction. PMID:24910597

  10. Modeling network traffic with the extreme value theory

    NASA Astrophysics Data System (ADS)

    Liu, Jiakun; Shu, Yantai; Yang, Oliver W. W.; Gao, Deyun

    2003-08-01

    This paper introduced the Extreme Value Theory (EVT) for analysis of network traffic. The role of EVT is to allow the development of procedures that are scientifically and statistically rational to estimate the extreme behavior of random processes. In this paper, we propose an EVT_based procedure to fit a model to the traffic trace. We have performed some simulation experiments on real-traffic traces such as video data to study the feasibility of our proposed method. Our experiments showed that the EVT method can be applied to statistical analysis of real traffic. Furthermore, since only the data greater than the threshold are processed, the computation overhead is reduced greatly. It indicates that EVT method could be applied to real time network control.

  11. Influence Function Learning in Information Diffusion Networks

    PubMed Central

    Du, Nan; Liang, Yingyu; Balcan, Maria-Florina; Song, Le

    2015-01-01

    Can we learn the influence of a set of people in a social network from cascades of information diffusion? This question is often addressed by a two-stage approach: first learn a diffusion model, and then calculate the influence based on the learned model. Thus, the success of this approach relies heavily on the correctness of the diffusion model which is hard to verify for real world data. In this paper, we exploit the insight that the influence functions in many diffusion models are coverage functions, and propose a novel parameterization of such functions using a convex combination of random basis functions. Moreover, we propose an efficient maximum likelihood based algorithm to learn such functions directly from cascade data, and hence bypass the need to specify a particular diffusion model in advance. We provide both theoretical and empirical analysis for our approach, showing that the proposed approach can provably learn the influence function with low sample complexity, be robust to the unknown diffusion models, and significantly outperform existing approaches in both synthetic and real world data. PMID:25973445

  12. Response to Patrick Love's "Informal Theory": A Rejoinder

    ERIC Educational Resources Information Center

    Evans, Nancy J.; Guido, Florence M.

    2012-01-01

    This rejoinder to Patrick Love's article, "Informal Theory: The Ignored Link in Theory-to-Practice," which appears earlier in this issue of the "Journal of College Student Development", was written at the invitation of the Editor. In the critique, we point out the weaknesses of many of Love's arguments and propositions. We provide an alternative…

  13. Information and communication theory. Citations from the NTIS data base

    NASA Astrophysics Data System (ADS)

    Carrigan, B.

    1980-04-01

    This bibliography cites Government sponsored research information and communication theory, including coding, decoding, and transmission of signals. Individual studies are cited on radio, television, and digital communication systems. Pure theory is also included. This updated bibliography contains 187 abstracts, 78 of which are new entries to the previous edition.

  14. Cartographic generalization of urban street networks based on gravitational field theory

    NASA Astrophysics Data System (ADS)

    Liu, Gang; Li, Yongshu; Li, Zheng; Guo, Jiawei

    2014-05-01

    The automatic generalization of urban street networks is a constant and important aspect of geographical information science. Previous studies show that the dual graph for street-street relationships more accurately reflects the overall morphological properties and importance of streets than do other methods. In this study, we construct a dual graph to represent street-street relationship and propose an approach to generalize street networks based on gravitational field theory. We retain the global structural properties and topological connectivity of an original street network and borrow from gravitational field theory to define the gravitational force between nodes. The concept of multi-order neighbors is introduced and the gravitational force is taken as the measure of the importance contribution between nodes. The importance of a node is defined as the result of the interaction between a given node and its multi-order neighbors. Degree distribution is used to evaluate the level of maintaining the global structure and topological characteristics of a street network and to illustrate the efficiency of the suggested method. Experimental results indicate that the proposed approach can be used in generalizing street networks and retaining their density characteristics, connectivity and global structure.

  15. Stochastical modeling for Viral Disease: Statistical Mechanics and Network Theory

    NASA Astrophysics Data System (ADS)

    Zhou, Hao; Deem, Michael

    2007-04-01

    Theoretical methods of statistical mechanics are developed and applied to study the immunological response against viral disease, such as dengue. We use this theory to show how the immune response to four different dengue serotypes may be sculpted. It is the ability of avian influenza, to change and to mix, that has given rise to the fear of a new human flu pandemic. Here we propose to utilize a scale free network based stochastic model to investigate the mitigation strategies and analyze the risk.

  16. A danger-theory-based immune network optimization algorithm.

    PubMed

    Zhang, Ruirui; Li, Tao; Xiao, Xin; Shi, Yuanquan

    2013-01-01

    Existing artificial immune optimization algorithms reflect a number of shortcomings, such as premature convergence and poor local search ability. This paper proposes a danger-theory-based immune network optimization algorithm, named dt-aiNet. The danger theory emphasizes that danger signals generated from changes of environments will guide different levels of immune responses, and the areas around danger signals are called danger zones. By defining the danger zone to calculate danger signals for each antibody, the algorithm adjusts antibodies' concentrations through its own danger signals and then triggers immune responses of self-regulation. So the population diversity can be maintained. Experimental results show that the algorithm has more advantages in the solution quality and diversity of the population. Compared with influential optimization algorithms, CLONALG, opt-aiNet, and dopt-aiNet, the algorithm has smaller error values and higher success rates and can find solutions to meet the accuracies within the specified function evaluation times.

  17. TOWARDS A SUSTAINABILITY INDEX USING INFORMATION THEORY

    EPA Science Inventory

    We explore the use of Fisher Information as a basis for an index of sustainability. Sustainability of an ecosystem refers to the robustness of a preferred dynamic regime to human and natural disturbances. Ecosystems under perturbations of varying regularity and intensity can ei...

  18. Objectivism in Information Utilization: Theory and Measurement.

    ERIC Educational Resources Information Center

    Leary, Mark R.; And Others

    A self-report scale was constructed and validated that measures individual differences in objectivism--the tendency to base one's judgments and beliefs upon empirical information and rational considerations. Validity data showed that, compared to people who score low on the Objectivism Scale, highly objective individuals enjoy thinking more, rely…

  19. A Progress Report on the Agricultural Sciences Information Network.

    ERIC Educational Resources Information Center

    National Agricultural Library (USDA), Washington, DC.

    In October 1970, the Agricultural Sciences Information Network (ASIN) Committee was established by the joint landgrant/USDA Agriculture Research Policy Advisory Committee (ARPAC). The ASIN Committee was directed to review various network concepts as a means of improving information services to professional agriculturalists in the private and…

  20. Instructional Technology: The Information Superhighway, the Internet, Interactive Video Networks.

    ERIC Educational Resources Information Center

    Odell, Kerry S.; And Others

    1994-01-01

    Includes "It Boggles the Mind" (Odell); "Merging Your Classroom onto the Information Superhighway" (Murphy); "The World's Largest Computer Network" (Fleck); "The Information Highway in Iowa" (Miller); "Interactive Video Networks in Secondary Schools" (Swan et al.); and "Upgrade to Humancentric Technology" (Berry). (JOW)

  1. Blending Formal and Informal Learning Networks for Online Learning

    ERIC Educational Resources Information Center

    Czerkawski, Betül C.

    2016-01-01

    With the emergence of social software and the advance of web-based technologies, online learning networks provide invaluable opportunities for learning, whether formal or informal. Unlike top-down, instructor-centered, and carefully planned formal learning settings, informal learning networks offer more bottom-up, student-centered participatory…

  2. Syntactic networks, do they contribute valid information on syntactic development in children?. Comment on "Approaching human language with complex networks" by J. Cong and H. Liu

    NASA Astrophysics Data System (ADS)

    Ninio, Anat

    2014-12-01

    In the target article [1] Cong and Liu provide a clear and informative introduction to the use of complex networks in research studying language. I would like to add the perspective of a researcher of language acquisition who has been hopeful that network theory illuminates processes of development [2,3], but feels a certain difficulty with studies applying network analysis to the development of syntax.

  3. Connectomics and graph theory analyses: Novel insights into network abnormalities in epilepsy.

    PubMed

    Gleichgerrcht, Ezequiel; Kocher, Madison; Bonilha, Leonardo

    2015-11-01

    The assessment of neural networks in epilepsy has become increasingly relevant in the context of translational research, given that localized forms of epilepsy are more likely to be related to abnormal function within specific brain networks, as opposed to isolated focal brain pathology. It is notable that variability in clinical outcomes from epilepsy treatment may be a reflection of individual patterns of network abnormalities. As such, network endophenotypes may be important biomarkers for the diagnosis and treatment of epilepsy. Despite its exceptional potential, measuring abnormal networks in translational research has been thus far constrained by methodologic limitations. Fortunately, recent advancements in neuroscience, particularly in the field of connectomics, permit a detailed assessment of network organization, dynamics, and function at an individual level. Data from the personal connectome can be assessed using principled forms of network analyses based on graph theory, which may disclose patterns of organization that are prone to abnormal dynamics and epileptogenesis. Although the field of connectomics is relatively new, there is already a rapidly growing body of evidence to suggest that it can elucidate several important and fundamental aspects of abnormal networks to epilepsy. In this article, we provide a review of the emerging evidence from connectomics research regarding neural network architecture, dynamics, and function related to epilepsy. We discuss how connectomics may bring together pathophysiologic hypotheses from conceptual and basic models of epilepsy and in vivo biomarkers for clinical translational research. By providing neural network information unique to each individual, the field of connectomics may help to elucidate variability in clinical outcomes and open opportunities for personalized medicine approaches to epilepsy. Connectomics involves complex and rich data from each subject, thus collaborative efforts to enable the

  4. Quantifying networks complexity from information geometry viewpoint

    SciTech Connect

    Felice, Domenico Mancini, Stefano; Pettini, Marco

    2014-04-15

    We consider a Gaussian statistical model whose parameter space is given by the variances of random variables. Underlying this model we identify networks by interpreting random variables as sitting on vertices and their correlations as weighted edges among vertices. We then associate to the parameter space a statistical manifold endowed with a Riemannian metric structure (that of Fisher-Rao). Going on, in analogy with the microcanonical definition of entropy in Statistical Mechanics, we introduce an entropic measure of networks complexity. We prove that it is invariant under networks isomorphism. Above all, considering networks as simplicial complexes, we evaluate this entropy on simplexes and find that it monotonically increases with their dimension.

  5. Biogeography revisited with network theory: retracing the history of hydrothermal vent communities.

    PubMed

    Moalic, Yann; Desbruyères, Daniel; Duarte, Carlos M; Rozenfeld, Alejandro F; Bachraty, Charleyne; Arnaud-Haond, Sophie

    2012-01-01

    Defining biogeographic provinces to understand the history and evolution of communities associated with a given kind of ecosystem is challenging and usually requires a priori assumptions to be made. We applied network theory, a holistic and exploratory method, to the most complete database of faunal distribution available on oceanic hydrothermal vents, environments which support fragmented and unstable ecosystems, to infer the processes driving their worldwide biogeography. Besides the identification of robust provinces, the network topology allowed us to identify preferential pathways that had hitherto been overlooked. These pathways are consistent with the previously proposed hypothesis of a role of plate tectonics in the biogeographical history of hydrothermal vent communities. A possible ancestral position of the Western Pacific is also suggested for the first time. Finally, this work provides an innovative example of the potential of network tools to unravel the biogeographic history of faunal assemblages and to supply comprehensive information for the conservation and management of biodiversity.

  6. Biogeography revisited with network theory: retracing the history of hydrothermal vent communities.

    PubMed

    Moalic, Yann; Desbruyères, Daniel; Duarte, Carlos M; Rozenfeld, Alejandro F; Bachraty, Charleyne; Arnaud-Haond, Sophie

    2012-01-01

    Defining biogeographic provinces to understand the history and evolution of communities associated with a given kind of ecosystem is challenging and usually requires a priori assumptions to be made. We applied network theory, a holistic and exploratory method, to the most complete database of faunal distribution available on oceanic hydrothermal vents, environments which support fragmented and unstable ecosystems, to infer the processes driving their worldwide biogeography. Besides the identification of robust provinces, the network topology allowed us to identify preferential pathways that had hitherto been overlooked. These pathways are consistent with the previously proposed hypothesis of a role of plate tectonics in the biogeographical history of hydrothermal vent communities. A possible ancestral position of the Western Pacific is also suggested for the first time. Finally, this work provides an innovative example of the potential of network tools to unravel the biogeographic history of faunal assemblages and to supply comprehensive information for the conservation and management of biodiversity. PMID:21856628

  7. Integrated information theory: from consciousness to its physical substrate.

    PubMed

    Tononi, Giulio; Boly, Melanie; Massimini, Marcello; Koch, Christof

    2016-07-01

    In this Opinion article, we discuss how integrated information theory accounts for several aspects of the relationship between consciousness and the brain. Integrated information theory starts from the essential properties of phenomenal experience, from which it derives the requirements for the physical substrate of consciousness. It argues that the physical substrate of consciousness must be a maximum of intrinsic cause-effect power and provides a means to determine, in principle, the quality and quantity of experience. The theory leads to some counterintuitive predictions and can be used to develop new tools for assessing consciousness in non-communicative patients.

  8. A quantitative approach to measure road network information based on edge diversity

    NASA Astrophysics Data System (ADS)

    Wu, Xun; Zhang, Hong; Lan, Tian; Cao, Weiwei; He, Jing

    2015-12-01

    The measure of map information has been one of the key issues in assessing cartographic quality and map generalization algorithms. It is also important for developing efficient approaches to transfer geospatial information. Road network is the most common linear object in real world. Approximately describe road network information will benefit road map generalization, navigation map production and urban planning. Most of current approaches focused on node diversities and supposed that all the edges are the same, which is inconsistent to real-life condition, and thus show limitations in measuring network information. As real-life traffic flow are directed and of different quantities, the original undirected vector road map was first converted to a directed topographic connectivity map. Then in consideration of preferential attachment in complex network study and rich-club phenomenon in social network, the from and to weights of each edge are assigned. The from weight of a given edge is defined as the connectivity of its end node to the sum of the connectivities of all the neighbors of the from nodes of the edge. After getting the from and to weights of each edge, edge information, node information and the whole network structure information entropies could be obtained based on information theory. The approach has been applied to several 1 square mile road network samples. Results show that information entropies based on edge diversities could successfully describe the structural differences of road networks. This approach is a complementarity to current map information measurements, and can be extended to measure other kinds of geographical objects.

  9. Applications of Information Theory for Ecohydrology Model Diagnostics

    NASA Astrophysics Data System (ADS)

    Ruddell, B. L.; Drewry, D.

    2013-12-01

    Earth System Models are becoming more complicated and complex as detailed formulations of physical and biological processes operating at multiple scales are integrated together to simulate the connections and feedbacks of the whole system. A prime example of this increase in process fidelity is the terrestrial land surface, where meteorological and hydrological processes drive and interact with the biological functioning of vegetation, together controlling carbon, water, and energy fluxes with the atmosphere. Ecohydrological models that capture these couplings and feedbacks may intentionally or unintentionally create self-organizing or "emergent" dynamics that do not exist when a single model component is used in isolation. It is therefore critical that model diagnostics begin to directly inspect model output for its fidelity to emergent system-scale patterns including observed couplings, feedbacks, thresholds, and controls. Information Theory provides a general class of methods that are able to directly measure coupling, control, and feedback. We apply these methods to compare observations and model results in the context of the Midwest US agro-ecosystem. We utilize a state-of-the-art ecohydrological model, MLCan, which has been extensively validated against eddy covariance observations of carbon, water and energy exchange collected at the Bondville, Illinois FLUXNET site. Using a dynamical process network approach in which system couplings are resolved as directional information flows, we show that MLCan does well at reproducing observed system-scale couplings, feedbacks, thresholds, and controls. We identify important exceptions that point to necessary model improvements. By applying these methods in addition to the standard residual error analysis, it is possible to move beyond asking whether an Earth System Model gets the "right answers", and to instead examine whether the model captures the emergent system-scale structures necessary to be correct for the

  10. OPLIN: The Ontario Public Library Information Network.

    ERIC Educational Resources Information Center

    Campbell, Bonnie

    1988-01-01

    Describes a network for interlibrary loans among public libraries in Ontario which includes microcomputer workstations, online searching of remote databases for verification and locations, and the use of electronic mail. The discussion covers the pilot project, its evaluation, cost effectiveness, extension of the network, and future plans.…

  11. Weather information network including graphical display

    NASA Technical Reports Server (NTRS)

    Leger, Daniel R. (Inventor); Burdon, David (Inventor); Son, Robert S. (Inventor); Martin, Kevin D. (Inventor); Harrison, John (Inventor); Hughes, Keith R. (Inventor)

    2006-01-01

    An apparatus for providing weather information onboard an aircraft includes a processor unit and a graphical user interface. The processor unit processes weather information after it is received onboard the aircraft from a ground-based source, and the graphical user interface provides a graphical presentation of the weather information to a user onboard the aircraft. Preferably, the graphical user interface includes one or more user-selectable options for graphically displaying at least one of convection information, turbulence information, icing information, weather satellite information, SIGMET information, significant weather prognosis information, and winds aloft information.

  12. A proposed concept for a crustal dynamics information management network

    NASA Technical Reports Server (NTRS)

    Lohman, G. M.; Renfrow, J. T.

    1980-01-01

    The findings of a requirements and feasibility analysis of the present and potential producers, users, and repositories of space-derived geodetic information are summarized. A proposed concept is presented for a crustal dynamics information management network that would apply state of the art concepts of information management technology to meet the expanding needs of the producers, users, and archivists of this geodetic information.

  13. Information Diffusion in Facebook-Like Social Networks Under Information Overload

    NASA Astrophysics Data System (ADS)

    Li, Pei; Xing, Kai; Wang, Dapeng; Zhang, Xin; Wang, Hui

    2013-07-01

    Research on social networks has received remarkable attention, since many people use social networks to broadcast information and stay connected with their friends. However, due to the information overload in social networks, it becomes increasingly difficult for users to find useful information. This paper takes Facebook-like social networks into account, and models the process of information diffusion under information overload. The term view scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated is proposed to characterize the information diffusion efficiency. Through theoretical analysis, we find that factors such as network structure and view scope number have no impact on the information diffusion efficiency, which is a surprising result. To verify the results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly.

  14. Information Flow Between Resting-State Networks

    PubMed Central

    Diez, Ibai; Erramuzpe, Asier; Escudero, Iñaki; Mateos, Beatriz; Cabrera, Alberto; Marinazzo, Daniele; Sanz-Arigita, Ernesto J.; Stramaglia, Sebastiano

    2015-01-01

    Abstract The resting brain dynamics self-organize into a finite number of correlated patterns known as resting-state networks (RSNs). It is well known that techniques such as independent component analysis can separate the brain activity at rest to provide such RSNs, but the specific pattern of interaction between RSNs is not yet fully understood. To this aim, we propose here a novel method to compute the information flow (IF) between different RSNs from resting-state magnetic resonance imaging. After hemodynamic response function blind deconvolution of all voxel signals, and under the hypothesis that RSNs define regions of interest, our method first uses principal component analysis to reduce dimensionality in each RSN to next compute IF (estimated here in terms of transfer entropy) between the different RSNs by systematically increasing k (the number of principal components used in the calculation). When k=1, this method is equivalent to computing IF using the average of all voxel activities in each RSN. For k≥1, our method calculates the k multivariate IF between the different RSNs. We find that the average IF among RSNs is dimension dependent, increasing from k=1 (i.e., the average voxel activity) up to a maximum occurring at k=5 and to finally decay to zero for k≥10. This suggests that a small number of components (close to five) is sufficient to describe the IF pattern between RSNs. Our method—addressing differences in IF between RSNs for any generic data—can be used for group comparison in health or disease. To illustrate this, we have calculated the inter-RSN IF in a data set of Alzheimer's disease (AD) to find that the most significant differences between AD and controls occurred for k=2, in addition to AD showing increased IF w.r.t. controls. The spatial localization of the k=2 component, within RSNs, allows the characterization of IF differences between AD and controls. PMID:26177254

  15. Searching for realism, structure and agency in Actor Network Theory.

    PubMed

    Elder-Vass, Dave

    2008-09-01

    Superficially, Actor Network Theory (ANT) and critical realism (CR) are radically opposed research traditions. Written from a realist perspective, this paper asks whether there might be a basis for finding common ground between these two traditions. It looks in turn at the questions of realism, structure, and agency, analysing the differences between the two perspectives and seeking to identify what each might learn from the other. Overall, the paper argues that there is a great deal that realists can learn from actor network theory; yet ANT remains stunted by its lack of a depth ontology. It fails to recognize the significance of mechanisms, and of their dependence on emergence, and thus lacks both dimensions of the depth that is characteristic of critical realism's ontology. This prevents ANT from recognizing the role and powers of social structure; but on the other hand, realists would do well to heed ANT's call for us to trace the connections through which structures are constantly made and remade. A lack of ontological depth also underpins ANT's practice of treating human and non-human actors symmetrically, yet this remains a valuable provocation to sociologists who neglect non-human entities entirely.

  16. Network selection, Information filtering and Scalable computation

    NASA Astrophysics Data System (ADS)

    Ye, Changqing

    This dissertation explores two application scenarios of sparsity pursuit method on large scale data sets. The first scenario is classification and regression in analyzing high dimensional structured data, where predictors corresponds to nodes of a given directed graph. This arises in, for instance, identification of disease genes for the Parkinson's diseases from a network of candidate genes. In such a situation, directed graph describes dependencies among the genes, where direction of edges represent certain causal effects. Key to high-dimensional structured classification and regression is how to utilize dependencies among predictors as specified by directions of the graph. In this dissertation, we develop a novel method that fully takes into account such dependencies formulated through certain nonlinear constraints. We apply the proposed method to two applications, feature selection in large margin binary classification and in linear regression. We implement the proposed method through difference convex programming for the cost function and constraints. Finally, theoretical and numerical analyses suggest that the proposed method achieves the desired objectives. An application to disease gene identification is presented. The second application scenario is personalized information filtering which extracts the information specifically relevant to a user, predicting his/her preference over a large number of items, based on the opinions of users who think alike or its content. This problem is cast into the framework of regression and classification, where we introduce novel partial latent models to integrate additional user-specific and content-specific predictors, for higher predictive accuracy. In particular, we factorize a user-over-item preference matrix into a product of two matrices, each representing a user's preference and an item preference by users. Then we propose a likelihood method to seek a sparsest latent factorization, from a class of over

  17. Transportation and dynamic networks: Models, theory, and applications to supply chains, electric power, and financial networks

    NASA Astrophysics Data System (ADS)

    Liu, Zugang

    Network systems, including transportation and logistic systems, electric power generation and distribution networks as well as financial networks, provide the critical infrastructure for the functioning of our societies and economies. The understanding of the dynamic behavior of such systems is also crucial to national security and prosperity. The identification of new connections between distinct network systems is the inspiration for the research in this dissertation. In particular, I answer two questions raised by Beckmann, McGuire, and Winsten (1956) and Copeland (1952) over half a century ago, which are, respectively, how are electric power flows related to transportation flows and does money flow like water or electricity? In addition, in this dissertation, I achieve the following: (1) I establish the relationships between transportation networks and three other classes of complex network systems: supply chain networks, electric power generation and transmission networks, and financial networks with intermediation. The establishment of such connections provides novel theoretical insights as well as new pricing mechanisms, and efficient computational methods. (2) I develop new modeling frameworks based on evolutionary variational inequality theory that capture the dynamics of such network systems in terms of the time-varying flows and incurred costs, prices, and, where applicable, profits. This dissertation studies the dynamics of such network systems by addressing both internal competition and/or cooperation, and external changes, such as varying costs and demands. (3) I focus, in depth, on electric power supply chains. By exploiting the relationships between transportation networks and electric power supply chains, I develop a large-scale network model that integrates electric power supply chains and fuel supply markets. The model captures both the economic transactions as well as the physical transmission constraints. The model is then applied to the New

  18. Mean-field theory of assortative networks of phase oscillators

    NASA Astrophysics Data System (ADS)

    Restrepo, Juan G.; Ott, Edward

    2014-09-01

    Employing the Kuramoto model as an illustrative example, we show how the use of the mean-field approximation can be applied to large networks of phase oscillators with assortativity. We then use the ansatz of Ott and Antonsen (Chaos, 19 (2008) 037113) to reduce the mean-field kinetic equations to a system of ordinary differential equations. The resulting formulation is illustrated by application to a network Kuramoto problem with degree assortativity and correlation between the node degrees and the natural oscillation frequencies. Good agreement is found between the solutions of the reduced set of ordinary differential equations obtained from our theory and full simulations of the system. These results highlight the ability of our method to capture all the phase transitions (bifurcations) and system attractors. One interesting result is that degree assortativity can induce transitions from a steady macroscopic state to a temporally oscillating macroscopic state through both (presumed) Hopf and SNIPER (saddle-node, infinite period) bifurcations. Possible use of these techniques to a broad class of phase oscillator network problems is discussed.

  19. Cooperation and information replication in wireless networks.

    PubMed

    Poularakis, Konstantinos; Tassiulas, Leandros

    2016-03-01

    A significant portion of today's network traffic is due to recurring downloads of a few popular contents. It has been observed that replicating the latter in caches installed at network edges-close to users-can drastically reduce network bandwidth usage and improve content access delay. Such caching architectures are gaining increasing interest in recent years as a way of dealing with the explosive traffic growth, fuelled further by the downward slope in storage space price. In this work, we provide an overview of caching with a particular emphasis on emerging network architectures that enable caching at the radio access network. In this context, novel challenges arise due to the broadcast nature of the wireless medium, which allows simultaneously serving multiple users tuned into a multicast stream, and the mobility of the users who may be frequently handed off from one cell tower to another. Existing results indicate that caching at the wireless edge has a great potential in removing bottlenecks on the wired backbone networks. Taking into consideration the schedule of multicast service and mobility profiles is crucial to extract maximum benefit in network performance. PMID:26809574

  20. Quantum metrology from an information theory perspective

    SciTech Connect

    Boixo, Sergio; Datta, Animesh; Davis, Matthew J.; Flammia, Steven T.; Shaji, Anil; Tacla, Alexandre B.; Caves, Carlton M.

    2009-04-13

    Questions about quantum limits on measurement precision were once viewed from the perspective of how to reduce or avoid the effects of quantum noise. With the advent of quantum information science came a paradigm shift to proving rigorous bounds on measurement precision. These bounds have been interpreted as saying, first, that the best achievable sensitivity scales as 1/n, where n is the number of particles one has available for a measurement and, second, that the only way to achieve this Heisenberg-limited sensitivity is to use quantum entanglement. We review these results and show that using quadratic couplings of n particles to a parameter to be estimated, one can achieve sensitivities that scale as 1/n{sup 2} if one uses entanglement, but even in the absence of any entanglement at any time during the measurement protocol, one can achieve a super-Heisenberg scaling of 1/n{sup 3/2}.

  1. Year 7 Students, Information Literacy, and Transfer: A Grounded Theory

    ERIC Educational Resources Information Center

    Herring, James E.

    2011-01-01

    This study examined the views of year 7 students, teacher librarians, and teachers in three state secondary schools in rural New South Wales, Australia, on information literacy and transfer. The aims of the study included the development of a grounded theory in relation to information literacy and transfer in these schools. The study's perspective…

  2. An Information-Related Systems Theory of Counseling

    ERIC Educational Resources Information Center

    Wilkinson, Melvin

    1973-01-01

    The author suggests that a systems theory of counseling should have an adequate theoretical foundation in the way various types of systems handle information. Using the works of W. R. Ashby as a basis, the paper is a beginning attempt to establish this foundation. It describes how information can be used to maintain homeostasis and protect key…

  3. Conceptions and Practice of Information Literacy in Academic Libraries: Espoused Theories and Theories-in-Use

    ERIC Educational Resources Information Center

    Kerr, Paulette A.

    2010-01-01

    This research was conducted to investigate the relationships between conceptions and practice of information literacy in academic libraries. To create a structure for the investigation, the research adopted the framework of Argyris and Schon (1974) in which professional practice is examined via theories of action, namely espoused theories and…

  4. Opportunistic Information Retrieval in Sparsely Connected Ad Hoc Networks

    NASA Astrophysics Data System (ADS)

    Chuah, Mooi-Choo; Han, Jian-Bin

    With the advancement in technology, many users carry wireless computing de-vices e.g., PDAs, cell-phones etc. Such devices can form mobile ad hoc networks and communicate with one another via the help of intermediate nodes. Such ad hoc networks are very useful in several scenarios e.g., battlefield operations, vehicular ad hoc networks and disaster response scenarios. The ability to access important information in these scenarios is highly critical. Many ad hoc routing schemes have been designed for ad hoc networks but such routing schemes are not useful in some challenging network scenarios where the nodes have intermittent connectivity and suffer frequent partitioning. Recently, disruption tolerant network technologies [5, 12] have been proposed to allow nodes in such extreme network-ing environment to communicate with one another. Several DTN routing schemes [4, 14] have been proposed.

  5. An integration of integrated information theory with fundamental physics.

    PubMed

    Barrett, Adam B

    2014-01-01

    To truly eliminate Cartesian ghosts from the science of consciousness, we must describe consciousness as an aspect of the physical. Integrated Information Theory states that consciousness arises from intrinsic information generated by dynamical systems; however existing formulations of this theory are not applicable to standard models of fundamental physical entities. Modern physics has shown that fields are fundamental entities, and in particular that the electromagnetic field is fundamental. Here I hypothesize that consciousness arises from information intrinsic to fundamental fields. This hypothesis unites fundamental physics with what we know empirically about the neuroscience underlying consciousness, and it bypasses the need to consider quantum effects.

  6. Networks of informal caring: a mixed-methods approach.

    PubMed

    Rutherford, Alasdair; Bowes, Alison

    2014-12-01

    Care for older people is a complex phenomenon, and is an area of pressing policy concern. Bringing together literature on care from social gerontology and economics, we report the findings of a mixed-methods project exploring networks of informal caring. Using quantitative data from the British Household Panel Survey (official survey of British households), together with qualitative interviews with older people and informal carers, we describe differences in formal care networks, and the factors and decision-making processes that have contributed to the formation of the networks. A network approach to care permits both quantitative and qualitative study, and the approach can be used to explore many important questions.

  7. Implications of information theory in optical fibre communications.

    PubMed

    Agrell, Erik; Alvarado, Alex; Kschischang, Frank R

    2016-03-01

    Recent decades have witnessed steady improvements in our ability to harness the information-carrying capability of optical fibres. Will this process continue, or will progress eventually stall? Information theory predicts that all channels have a limited capacity depending on the available transmission resources, and thus it is inevitable that the pace of improvements will slow. However, information theory also provides insights into how transmission resources should, in principle, best be exploited, and thus may serve as a guide for where to look for better ways to squeeze more out of a precious resource. This tutorial paper reviews the basic concepts of information theory and their application in fibre-optic communications. PMID:26809578

  8. Affinity based information diffusion model in social networks

    NASA Astrophysics Data System (ADS)

    Liu, Hongli; Xie, Yun; Hu, Haibo; Chen, Zhigao

    2014-12-01

    There is a widespread intuitive sense that people prefer participating in spreading the information in which they are interested. The affinity of people with information disseminated can affect the information propagation in social networks. In this paper, we propose an information diffusion model incorporating the mechanism of affinity of people with information which considers the fitness of affinity values of people with affinity threshold of the information. We find that the final size of information diffusion is affected by affinity threshold of the information, average degree of the network and the probability of people's losing their interest in the information. We also explore the effects of other factors on information spreading by numerical simulations and find that the probabilities of people's questioning and confirming the information can affect the propagation speed, but not the final scope.

  9. Complex Dynamics in Information Sharing Networks

    NASA Astrophysics Data System (ADS)

    Cronin, Bruce

    This study examines the roll-out of an electronic knowledge base in a medium-sized professional services firm over a six year period. The efficiency of such implementation is a key business problem in IT systems of this type. Data from usage logs provides the basis for analysis of the dynamic evolution of social networks around the depository during this time. The adoption pattern follows an "s-curve" and usage exhibits something of a power law distribution, both attributable to network effects, and network position is associated with organisational performance on a number of indicators. But periodicity in usage is evident and the usage distribution displays an exponential cut-off. Further analysis provides some evidence of mathematical complexity in the periodicity. Some implications of complex patterns in social network data for research and management are discussed. The study provides a case study demonstrating the utility of the broad methodological approach.

  10. Analysing collaboration among HIV agencies through combining network theory and relational coordination.

    PubMed

    Khosla, Nidhi; Marsteller, Jill Ann; Hsu, Yea Jen; Elliott, David L

    2016-02-01

    Agencies with different foci (e.g. nutrition, social, medical, housing) serve people living with HIV (PLHIV). Serving needs of PLHIV comprehensively requires a high degree of coordination among agencies which often benefits from more frequent communication. We combined Social Network theory and Relational Coordination theory to study coordination among HIV agencies in Baltimore. Social Network theory implies that actors (e.g., HIV agencies) establish linkages amongst themselves in order to access resources (e.g., information). Relational Coordination theory suggests that high quality coordination among agencies or teams relies on the seven dimensions of frequency, timeliness and accuracy of communication, problem-solving communication, knowledge of agencies' work, mutual respect and shared goals. We collected data on frequency of contact from 57 agencies using a roster method. Response options were ordinal ranging from 'not at all' to 'daily'. We analyzed data using social network measures. Next, we selected agencies with which at least one-third of the sample reported monthly or more frequent interaction. This yielded 11 agencies whom we surveyed on seven relational coordination dimensions with questions scored on a Likert scale of 1-5. Network density, defined as the proportion of existing connections to all possible connections, was 20% when considering monthly or higher interaction. Relational coordination scores from individual agencies to others ranged between 1.17 and 5.00 (maximum possible score 5). The average scores for different dimensions across all agencies ranged between 3.30 and 4.00. Shared goals (4.00) and mutual respect (3.91) scores were highest, while scores such as knowledge of each other's work and problem-solving communication were relatively lower. Combining theoretically driven analyses in this manner offers an innovative way to provide a comprehensive picture of inter-agency coordination and the quality of exchange that underlies

  11. Information Theory - The Bridge Connecting Bounded Rational Game Theory and Statistical Physics

    NASA Technical Reports Server (NTRS)

    Wolpert, David H.

    2005-01-01

    A long-running difficulty with conventional game theory has been how to modify it to accommodate the bounded rationality of all red-world players. A recurring issue in statistical physics is how best to approximate joint probability distributions with decoupled (and therefore far more tractable) distributions. This paper shows that the same information theoretic mathematical structure, known as Product Distribution (PD) theory, addresses both issues. In this, PD theory not only provides a principle formulation of bounded rationality and a set of new types of mean field theory in statistical physics; it also shows that those topics are fundamentally one and the same.

  12. The Network Information Management System (NIMS) in the Deep Space Network

    NASA Technical Reports Server (NTRS)

    Wales, K. J.

    1983-01-01

    In an effort to better manage enormous amounts of administrative, engineering, and management data that is distributed worldwide, a study was conducted which identified the need for a network support system. The Network Information Management System (NIMS) will provide the Deep Space Network with the tools to provide an easily accessible source of valid information to support management activities and provide a more cost-effective method of acquiring, maintaining, and retrieval data.

  13. Tensegrity II. How structural networks influence cellular information processing networks

    NASA Technical Reports Server (NTRS)

    Ingber, Donald E.

    2003-01-01

    The major challenge in biology today is biocomplexity: the need to explain how cell and tissue behaviors emerge from collective interactions within complex molecular networks. Part I of this two-part article, described a mechanical model of cell structure based on tensegrity architecture that explains how the mechanical behavior of the cell emerges from physical interactions among the different molecular filament systems that form the cytoskeleton. Recent work shows that the cytoskeleton also orients much of the cell's metabolic and signal transduction machinery and that mechanical distortion of cells and the cytoskeleton through cell surface integrin receptors can profoundly affect cell behavior. In particular, gradual variations in this single physical control parameter (cell shape distortion) can switch cells between distinct gene programs (e.g. growth, differentiation and apoptosis), and this process can be viewed as a biological phase transition. Part II of this article covers how combined use of tensegrity and solid-state mechanochemistry by cells may mediate mechanotransduction and facilitate integration of chemical and physical signals that are responsible for control of cell behavior. In addition, it examines how cell structural networks affect gene and protein signaling networks to produce characteristic phenotypes and cell fate transitions during tissue development.

  14. A worldwide population information network: status and goals.

    PubMed

    Kolbe, H K

    1978-07-01

    The rapid growth of world population and changes in government policies and programs have brought many changes to the area of population information. These include an increase in the amount of population information and funds devoted to research as well as an increase in awareness of the need to improve access to population information. Population information resources are located mainly in the developed countries, and no adequate information flow has yet been established to and from the developing nations. In response to this need, emerging regional population information networks are already identifiable. Focus is on components of an international population information network; North America and Europe; Latin America; Asia; Africa; and POPINS (worldwide population information system) Proposal, the model, and a counterproposal. It is evident that a strong North American European network is coalescing rapidly. The Latin American Population Documentation Systems (DOCPAL) offers the promise of bringing order to population information in Latin America. In Asia and Africa the situation in regard to population networks looks encouraging. During the next 2-year period the POPINS proposal will be carefully scrutinized. On the basis of these efforts, it seems reasonable to predict that within a 7-10 year period a de facto worldwide population information network will be a reality. PMID:10308566

  15. Incorporating profile information in community detection for online social networks

    NASA Astrophysics Data System (ADS)

    Fan, W.; Yeung, K. H.

    2014-07-01

    Community structure is an important feature in the study of complex networks. It is because nodes of the same community may have similar properties. In this paper we extend two popular community detection methods to partition online social networks. In our extended methods, the profile information of users is used for partitioning. We apply the extended methods in several sample networks of Facebook. Compared with the original methods, the community structures we obtain have higher modularity. Our results indicate that users' profile information is consistent with the community structure of their friendship network to some extent. To the best of our knowledge, this paper is the first to discuss how profile information can be used to improve community detection in online social networks.

  16. Complexity measurement based on information theory and kolmogorov complexity.

    PubMed

    Lui, Leong Ting; Terrazas, Germán; Zenil, Hector; Alexander, Cameron; Krasnogor, Natalio

    2015-01-01

    In the past decades many definitions of complexity have been proposed. Most of these definitions are based either on Shannon's information theory or on Kolmogorov complexity; these two are often compared, but very few studies integrate the two ideas. In this article we introduce a new measure of complexity that builds on both of these theories. As a demonstration of the concept, the technique is applied to elementary cellular automata and simulations of the self-organization of porphyrin molecules.

  17. Application of Game Theory Approaches in Routing Protocols for Wireless Networks

    NASA Astrophysics Data System (ADS)

    Javidi, Mohammad M.; Aliahmadipour, Laya

    2011-09-01

    An important and essential issue for wireless networks is routing protocol design that is a major technical challenge due to the function of the network. Game theory is a powerful mathematical tool that analyzes the strategic interactions among multiple decision makers and the results of researches show that applied game theory in routing protocol lead to improvement the network performance through reduce overhead and motivates selfish nodes to collaborate in the network. This paper presents a review and comparison for typical representatives of routing protocols designed that applied game theory approaches for various wireless networks such as ad hoc networks, mobile ad hoc networks and sensor networks that all of them lead to improve the network performance.

  18. Advanced information processing system: Input/output network management software

    NASA Technical Reports Server (NTRS)

    Nagle, Gail; Alger, Linda; Kemp, Alexander

    1988-01-01

    The purpose of this document is to provide the software requirements and specifications for the Input/Output Network Management Services for the Advanced Information Processing System. This introduction and overview section is provided to briefly outline the overall architecture and software requirements of the AIPS system before discussing the details of the design requirements and specifications of the AIPS I/O Network Management software. A brief overview of the AIPS architecture followed by a more detailed description of the network architecture.

  19. Grower Communication Networks: Information Sources for Organic Farmers

    ERIC Educational Resources Information Center

    Crawford, Chelsi; Grossman, Julie; Warren, Sarah T.; Cubbage, Fred

    2015-01-01

    This article reports on a study to determine which information sources organic growers use to inform farming practices by conducting in-depth semi-structured interviews with 23 organic farmers across 17 North Carolina counties. Effective information sources included: networking, agricultural organizations, universities, conferences, Extension, Web…

  20. Information Security Analysis Using Game Theory and Simulation

    SciTech Connect

    Schlicher, Bob G; Abercrombie, Robert K

    2012-01-01

    Information security analysis can be performed using game theory implemented in dynamic simulations of Agent Based Models (ABMs). Such simulations can be verified with the results from game theory analysis and further used to explore larger scale, real world scenarios involving multiple attackers, defenders, and information assets. Our approach addresses imperfect information and scalability that allows us to also address previous limitations of current stochastic game models. Such models only consider perfect information assuming that the defender is always able to detect attacks; assuming that the state transition probabilities are fixed before the game assuming that the players actions are always synchronous; and that most models are not scalable with the size and complexity of systems under consideration. Our use of ABMs yields results of selected experiments that demonstrate our proposed approach and provides a quantitative measure for realistic information systems and their related security scenarios.

  1. Emerging Communities: Integrating Networked Information into Library Services (Book Review).

    ERIC Educational Resources Information Center

    Afifi, Marianne

    1995-01-01

    Reviews this collection of papers, edited by Ann P. Bishop, which present the current state of networking as it relates to libraries and the community. Recommends the book as a compendium of lessons, learned and to be learned, as networked information becomes an integral and necessary part of the library world. (JMV)

  2. Are Social Networking Websites Educational? Information Capsule. Volume 0909

    ERIC Educational Resources Information Center

    Blazer, Christie

    2009-01-01

    More and more school districts across the country are joining social networking sites, such as Facebook and MySpace. This Information Capsule discusses the frequency with which school districts are using social networking sites, how districts are using the sites, and potential drawbacks associated with their use. Issues for districts to consider…

  3. Social contagion theory: examining dynamic social networks and human behavior.

    PubMed

    Christakis, Nicholas A; Fowler, James H

    2013-02-20

    Here, we review the research we have conducted on social contagion. We describe the methods we have employed (and the assumptions they have entailed) to examine several datasets with complementary strengths and weaknesses, including the Framingham Heart Study, the National Longitudinal Study of Adolescent Health, and other observational and experimental datasets that we and others have collected. We describe the regularities that led us to propose that human social networks may exhibit a 'three degrees of influence' property, and we review statistical approaches we have used to characterize interpersonal influence with respect to phenomena as diverse as obesity, smoking, cooperation, and happiness. We do not claim that this work is the final word, but we do believe that it provides some novel, informative, and stimulating evidence regarding social contagion in longitudinally followed networks. Along with other scholars, we are working to develop new methods for identifying causal effects using social network data, and we believe that this area is ripe for statistical development as current methods have known and often unavoidable limitations.

  4. Social contagion theory: examining dynamic social networks and human behavior

    PubMed Central

    Christakis, Nicholas A.; Fowler, James H.

    2013-01-01

    Here, we review the research we have conducted on social contagion. We describe the methods we have employed (and the assumptions they have entailed) to examine several datasets with complementary strengths and weaknesses, including the Framingham Heart Study, the National Longitudinal Study of Adolescent Health, and other observational and experimental datasets that we and others have collected. We describe the regularities that led us to propose that human social networks may exhibit a ‘three degrees of influence’ property, and we review statistical approaches we have used to characterize interpersonal influence with respect to phenomena as diverse as obesity, smoking, cooperation, and happiness. We do not claim that this work is the final word, but we do believe that it provides some novel, informative, and stimulating evidence regarding social contagion in longitudinally followed networks. Along with other scholars, we are working to develop new methods for identifying causal effects using social network data, and we believe that this area is ripe for statistical development as current methods have known and often unavoidable limitations. PMID:22711416

  5. Architectural Design for the Global Legal Information Network

    NASA Technical Reports Server (NTRS)

    Kalpakis, Konstantinos

    1999-01-01

    In this report, we provide a summary of our activities regarding the goals, requirements analysis, design, and prototype implementation for the Global Legal Information Network, a joint effort between the Law Library of Congress and NASA.

  6. The Changing Role in a Networked Information Environment.

    ERIC Educational Resources Information Center

    Lynch, Clifford A.

    1997-01-01

    Reviews traditional issues surrounding authorization and authentication in an organization-centered framework and introduces new interorganizational issues that dominate networked information environment. Describes three major approaches to authentication and authorization for the interorganizational environment and discusses the following…

  7. Tufts academic health information network: concept and scenario.

    PubMed

    Stearns, N S

    1986-04-01

    Tufts University School of Medicine's new health sciences education building, the Arthur M. Sackler Center for Health Communications, will house a modern medical library and computer center, classrooms, auditoria, and media facilities. The building will also serve as the center for an information and communication network linking the medical school and adjacent New England Medical Center, Tufts' primary teaching hospital, with Tufts Associated Teaching Hospitals throughout New England. Ultimately, the Tufts network will join other gateway networks, information resource facilities, health care institutions, and medical schools throughout the world. The center and the network are intended to facilitate and improve the education of health professionals, the delivery of health care to patients, the conduct of research, and the implementation of administrative management approaches that should provide more efficient utilization of resources and save dollars. A model and scenario show how health care delivery and health care education are integrated through better use of information transfer technologies by health information specialists, practitioners, and educators.

  8. Developing Visualization Techniques for Semantics-based Information Networks

    NASA Technical Reports Server (NTRS)

    Keller, Richard M.; Hall, David R.

    2003-01-01

    Information systems incorporating complex network structured information spaces with a semantic underpinning - such as hypermedia networks, semantic networks, topic maps, and concept maps - are being deployed to solve some of NASA s critical information management problems. This paper describes some of the human interaction and navigation problems associated with complex semantic information spaces and describes a set of new visual interface approaches to address these problems. A key strategy is to leverage semantic knowledge represented within these information spaces to construct abstractions and views that will be meaningful to the human user. Human-computer interaction methodologies will guide the development and evaluation of these approaches, which will benefit deployed NASA systems and also apply to information systems based on the emerging Semantic Web.

  9. Sender-receiver systems and applying information theory for quantitative synthetic biology.

    PubMed

    Barcena Menendez, Diego; Senthivel, Vivek Raj; Isalan, Mark

    2015-02-01

    Sender-receiver (S-R) systems abound in biology, with communication systems sending information in various forms. Information theory provides a quantitative basis for analysing these processes and is being applied to study natural genetic, enzymatic and neural networks. Recent advances in synthetic biology are providing us with a wealth of artificial S-R systems, giving us quantitative control over networks with a finite number of well-characterised components. Combining the two approaches can help to predict how to maximise signalling robustness, and will allow us to make increasingly complex biological computers. Ultimately, pushing the boundaries of synthetic biology will require moving beyond engineering the flow of information and towards building more sophisticated circuits that interpret biological meaning.

  10. Sender–receiver systems and applying information theory for quantitative synthetic biology

    PubMed Central

    Barcena Menendez, Diego; Senthivel, Vivek Raj; Isalan, Mark

    2015-01-01

    Sender–receiver (S–R) systems abound in biology, with communication systems sending information in various forms. Information theory provides a quantitative basis for analysing these processes and is being applied to study natural genetic, enzymatic and neural networks. Recent advances in synthetic biology are providing us with a wealth of artificial S–R systems, giving us quantitative control over networks with a finite number of well-characterised components. Combining the two approaches can help to predict how to maximise signalling robustness, and will allow us to make increasingly complex biological computers. Ultimately, pushing the boundaries of synthetic biology will require moving beyond engineering the flow of information and towards building more sophisticated circuits that interpret biological meaning. PMID:25282688

  11. 75 FR 44800 - Notice of Meeting of the Homeland Security Information Network Advisory Committee, Tuesday...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-07-29

    ... SECURITY Notice of Meeting of the Homeland Security Information Network Advisory Committee, Tuesday, August... meeting. SUMMARY: The Homeland Security Information Network Advisory Committee (HSINAC) will meet from... Homeland Security Information Network Advisory Committee is to identify issues and provide to...

  12. Exploring security and privacy issues in hospital information system: an Information Boundary Theory perspective.

    PubMed

    Zakaria, Nasriah; Stanton, Jeffrey; Stam, Kathryn

    2003-01-01

    A small community hospital (67 beds) in Central New York was undergoing a major technological change within the organization, as they move from the use of several legacy information systems to a hospital-wide information system. The focus of the present research is to explore the privacy and security information issues using a framework called Information Boundary Theory [Stanton, 2002]. IBT explains the motivational factors that lead to the revelation or disclosing of information.

  13. Order from noise: Toward a social theory of geographic information

    USGS Publications Warehouse

    Poore, B.S.; Chrisman, N.R.

    2006-01-01

    In the so-called Information Age, it is surprising that the concept of information is imprecisely defined and almost taken for granted. Historic and recent geographic information science (GIScience) literature relies on two conflicting metaphors, often espoused by the same author in adjacent paragraphs. The metaphor of invariance, derived from telecommunications engineering, defines information as a thing to be transported without loss through a conduit. Another metaphor, originating in the utopian movements of the 19th century, locates information within a hierarchy of refinement-a stopping place on the path to convert mere data into higher forms of knowledge and perhaps to wisdom. Both metaphors rely on long-forgotten debates outside geography and preclude us from seeing that there are important social and ethical concerns in the relationship between geographic information technologies and society. We examine the conflicts between competing metaphors and propose a social theory of geographic information. ?? 2006 by Association of American Geographers.

  14. Feasibility Evaluation of an On-site Generator Network by the Cooperative Game Theory

    NASA Astrophysics Data System (ADS)

    Komiyama, Ryoichi; Hayashi, Taketo; Fujii, Yasumasa; Yamaji, Kenji

    On-site generator, such as CGS (cogeneration system), is allegedly considered to be an effective end-use energy system in order to accomplish primary energy conservation, CO2 emission mitigation and system cost reduction, which characteristics eventually improve the whole performance of an existing energy system for the future. Considering the drawback of installing an end-use CGS into the customer with small or middle scale floor space, however, it is difficult to achieve those distinctive features because the thermal-electricity ratio of CGS does not always be in agreement with that of customer energy demand. In order to overcome that matching deficiency, it is hence better to organize an on-site generator network based on mutual electricity and heating transmission. But focusing on some cogenerators underlying their behaviors on maximizing their own profits, this on-site network, which situation corresponds to a grand coalition, is not necessarily established because of each cogenerator’s motivation to form a partial coalition and acquire its own profit as much as possible. In this paper, we attempt to analyze the optimal operation of an on-site generator network and identify by applying the nucleolus of the cooperative game theory the optimal benefit allocation strategy in order for the cogenerators to construct the network. Regarding the installation site of this network, the center of Tokyo area is assumed, which locational information includes floor space and so forth through a GIS (geographic information system) database. The results from the nucleolus suggest that all districts should impartially obtain the benefit from organizing network for the purpose of jointly attaining the system total cost reduction.

  15. EDITORIAL: Focus on Quantum Information and Many-Body Theory

    NASA Astrophysics Data System (ADS)

    Eisert, Jens; Plenio, Martin B.

    2010-02-01

    Quantum many-body models describing natural systems or materials and physical systems assembled piece by piece in the laboratory for the purpose of realizing quantum information processing share an important feature: intricate correlations that originate from the coherent interaction between a large number of constituents. In recent years it has become manifest that the cross-fertilization between research devoted to quantum information science and to quantum many-body physics leads to new ideas, methods, tools, and insights in both fields. Issues of criticality, quantum phase transitions, quantum order and magnetism that play a role in one field find relations to the classical simulation of quantum systems, to error correction and fault tolerance thresholds, to channel capacities and to topological quantum computation, to name but a few. The structural similarities of typical problems in both fields and the potential for pooling of ideas then become manifest. Notably, methods and ideas from quantum information have provided fresh approaches to long-standing problems in strongly correlated systems in the condensed matter context, including both numerical methods and conceptual insights. Focus on quantum information and many-body theory Contents TENSOR NETWORKS Homogeneous multiscale entanglement renormalization ansatz tensor networks for quantum critical systems M Rizzi, S Montangero, P Silvi, V Giovannetti and Rosario Fazio Concatenated tensor network states R Hübener, V Nebendahl and W Dür Entanglement renormalization in free bosonic systems: real-space versus momentum-space renormalization group transforms G Evenbly and G Vidal Finite-size geometric entanglement from tensor network algorithms Qian-Qian Shi, Román Orús, John Ove Fjærestad and Huan-Qiang Zhou Characterizing symmetries in a projected entangled pair state D Pérez-García, M Sanz, C E González-Guillén, M M Wolf and J I Cirac Matrix product operator representations B Pirvu, V Murg, J I Cirac

  16. EDITORIAL: Focus on Quantum Information and Many-Body Theory

    NASA Astrophysics Data System (ADS)

    Eisert, Jens; Plenio, Martin B.

    2010-02-01

    Quantum many-body models describing natural systems or materials and physical systems assembled piece by piece in the laboratory for the purpose of realizing quantum information processing share an important feature: intricate correlations that originate from the coherent interaction between a large number of constituents. In recent years it has become manifest that the cross-fertilization between research devoted to quantum information science and to quantum many-body physics leads to new ideas, methods, tools, and insights in both fields. Issues of criticality, quantum phase transitions, quantum order and magnetism that play a role in one field find relations to the classical simulation of quantum systems, to error correction and fault tolerance thresholds, to channel capacities and to topological quantum computation, to name but a few. The structural similarities of typical problems in both fields and the potential for pooling of ideas then become manifest. Notably, methods and ideas from quantum information have provided fresh approaches to long-standing problems in strongly correlated systems in the condensed matter context, including both numerical methods and conceptual insights. Focus on quantum information and many-body theory Contents TENSOR NETWORKS Homogeneous multiscale entanglement renormalization ansatz tensor networks for quantum critical systems M Rizzi, S Montangero, P Silvi, V Giovannetti and Rosario Fazio Concatenated tensor network states R Hübener, V Nebendahl and W Dür Entanglement renormalization in free bosonic systems: real-space versus momentum-space renormalization group transforms G Evenbly and G Vidal Finite-size geometric entanglement from tensor network algorithms Qian-Qian Shi, Román Orús, John Ove Fjærestad and Huan-Qiang Zhou Characterizing symmetries in a projected entangled pair state D Pérez-García, M Sanz, C E González-Guillén, M M Wolf and J I Cirac Matrix product operator representations B Pirvu, V Murg, J I Cirac

  17. Phase transitions for information diffusion in random clustered networks

    NASA Astrophysics Data System (ADS)

    Lim, Sungsu; Shin, Joongbo; Kwak, Namju; Jung, Kyomin

    2016-09-01

    We study the conditions for the phase transitions of information diffusion in complex networks. Using the random clustered network model, a generalisation of the Chung-Lu random network model incorporating clustering, we examine the effect of clustering under the Susceptible-Infected-Recovered (SIR) epidemic diffusion model with heterogeneous contact rates. For this purpose, we exploit the branching process to analyse information diffusion in random unclustered networks with arbitrary contact rates, and provide novel iterative algorithms for estimating the conditions and sizes of global cascades, respectively. Showing that a random clustered network can be mapped into a factor graph, which is a locally tree-like structure, we successfully extend our analysis to random clustered networks with heterogeneous contact rates. We then identify the conditions for phase transitions of information diffusion using our method. Interestingly, for various contact rates, we prove that random clustered networks with higher clustering coefficients have strictly lower phase transition points for any given degree sequence. Finally, we confirm our analytical results with numerical simulations of both synthetically-generated and real-world networks.

  18. Do Brain Networks Evolve by Maximizing Their Information Flow Capacity?

    PubMed Central

    Antonopoulos, Chris G.; Srivastava, Shambhavi; Pinto, Sandro E. de S.; Baptista, Murilo S.

    2015-01-01

    We propose a working hypothesis supported by numerical simulations that brain networks evolve based on the principle of the maximization of their internal information flow capacity. We find that synchronous behavior and capacity of information flow of the evolved networks reproduce well the same behaviors observed in the brain dynamical networks of Caenorhabditis elegans and humans, networks of Hindmarsh-Rose neurons with graphs given by these brain networks. We make a strong case to verify our hypothesis by showing that the neural networks with the closest graph distance to the brain networks of Caenorhabditis elegans and humans are the Hindmarsh-Rose neural networks evolved with coupling strengths that maximize information flow capacity. Surprisingly, we find that global neural synchronization levels decrease during brain evolution, reflecting on an underlying global no Hebbian-like evolution process, which is driven by no Hebbian-like learning behaviors for some of the clusters during evolution, and Hebbian-like learning rules for clusters where neurons increase their synchronization. PMID:26317592

  19. Evolutionary ultimatum game on complex networks under incomplete information

    NASA Astrophysics Data System (ADS)

    Bo, Xianyu; Yang, Jianmei

    2010-03-01

    This paper studies the evolutionary ultimatum game on networks when agents have incomplete information about the strategies of their neighborhood agents. Our model assumes that agents may initially display low fairness behavior, and therefore, may have to learn and develop their own strategies in this unknown environment. The Genetic Algorithm Learning Classifier System (GALCS) is used in the model as the agent strategy learning rule. Aside from the Watts-Strogatz (WS) small-world network and its variations, the present paper also extends the spatial ultimatum game to the Barabási-Albert (BA) scale-free network. Simulation results show that the fairness level achieved is lower than in situations where agents have complete information about other agents’ strategies. The research results display that fairness behavior will always emerge regardless of the distribution of the initial strategies. If the strategies are randomly distributed on the network, then the long-term agent fairness levels achieved are very close given unchanged learning parameters. Neighborhood size also has little effect on the fairness level attained. The simulation results also imply that WS small-world and BA scale-free networks have different effects on the spatial ultimatum game. In ultimatum game on networks with incomplete information, the WS small-world network and its variations favor the emergence of fairness behavior slightly more than the BA network where agents are heterogeneously structured.

  20. Phase transitions for information diffusion in random clustered networks

    NASA Astrophysics Data System (ADS)

    Lim, Sungsu; Shin, Joongbo; Kwak, Namju; Jung, Kyomin

    2016-08-01

    We study the conditions for the phase transitions of information diffusion in complex networks. Using the random clustered network model, a generalisation of the Chung-Lu random network model incorporating clustering, we examine the effect of clustering under the Susceptible-Infected-Recovered (SIR) epidemic diffusion model with heterogeneous contact rates. For this purpose, we exploit the branching process to analyse information diffusion in random unclustered networks with arbitrary contact rates, and provide novel iterative algorithms for estimating the conditions and sizes of global cascades, respectively. Showing that a random clustered network can be mapped into a factor graph, which is a locally tree-like structure, we successfully extend our analysis to random clustered networks with heterogeneous contact rates. We then identify the conditions for phase transitions of information diffusion using our method. Interestingly, for various contact rates, we prove that random clustered networks with higher clustering coefficients have strictly lower phase transition points for any given degree sequence. Finally, we confirm our analytical results with numerical simulations of both synthetically-generated and real-world networks.

  1. The Philosophy of Information as an Underlying and Unifying Theory of Information Science

    ERIC Educational Resources Information Center

    Tomic, Taeda

    2010-01-01

    Introduction: Philosophical analyses of theoretical principles underlying these sub-domains reveal philosophy of information as underlying meta-theory of information science. Method: Conceptual research on the knowledge sub-domains in information science and philosophy and analysis of their mutual connection. Analysis: Similarities between…

  2. Activity Theory in Information Systems Research and Practice: Theoretical Underpinnings for an Information Systems Development Model

    ERIC Educational Resources Information Center

    Mursu, Anja; Luukkonen, Irmeli; Toivanen, Marika; Korpela, Mikko

    2007-01-01

    Introduction: The purpose of information systems is to facilitate work activities: here we consider how Activity Theory can be applied in information systems development. Method. The requirements for an analytical model for emancipatory, work-oriented information systems research and practice are specified. Previous research work in Activity…

  3. Quantum information and gravity cutoff in theories with species

    NASA Astrophysics Data System (ADS)

    Dvali, Gia; Gomez, Cesar

    2009-04-01

    We show that lowering of the gravitational cutoff relative to the Planck mass, imposed by black hole physics in theories with N species, has an independent justification from quantum information theory. First, this scale marks the limiting capacity of any information processor. Secondly, by taking into the account the limitations of the quantum information storage in any system with species, the bound on the gravity cutoff becomes equivalent to the holographic bound, and this equivalence automatically implies the equality of entanglement and Bekenstein-Hawking entropies. Next, the same bound follows from quantum cloning theorem. Finally, we point out that by identifying the UV and IR threshold scales of the black hole quasi-classicality in four-dimensional field and high dimensional gravity theories, the bound translates as the correspondence between the two theories. In case when the high dimensional background is AdS, this reproduces the well-known AdS/CFT relation, but also suggests a generalization of the correspondence beyond AdS spaces. In particular, it reproduces a recently suggested duality between a four-dimensional CFT and a flat five-dimensional theory, in which gravity crosses over from four to five dimensional regime in far infrared.

  4. Competition between Homophily and Information Entropy Maximization in Social Networks

    PubMed Central

    Zhao, Jichang; Liang, Xiao; Xu, Ke

    2015-01-01

    In social networks, it is conventionally thought that two individuals with more overlapped friends tend to establish a new friendship, which could be stated as homophily breeding new connections. While the recent hypothesis of maximum information entropy is presented as the possible origin of effective navigation in small-world networks. We find there exists a competition between information entropy maximization and homophily in local structure through both theoretical and experimental analysis. This competition suggests that a newly built relationship between two individuals with more common friends would lead to less information entropy gain for them. We demonstrate that in the evolution of the social network, both of the two assumptions coexist. The rule of maximum information entropy produces weak ties in the network, while the law of homophily makes the network highly clustered locally and the individuals would obtain strong and trust ties. A toy model is also presented to demonstrate the competition and evaluate the roles of different rules in the evolution of real networks. Our findings could shed light on the social network modeling from a new perspective. PMID:26334994

  5. Social networks predict selective observation and information spread in ravens.

    PubMed

    Kulahci, Ipek G; Rubenstein, Daniel I; Bugnyar, Thomas; Hoppitt, William; Mikus, Nace; Schwab, Christine

    2016-07-01

    Animals are predicted to selectively observe and learn from the conspecifics with whom they share social connections. Yet, hardly anything is known about the role of different connections in observation and learning. To address the relationships between social connections, observation and learning, we investigated transmission of information in two raven (Corvus corax) groups. First, we quantified social connections in each group by constructing networks on affiliative interactions, aggressive interactions and proximity. We then seeded novel information by training one group member on a novel task and allowing others to observe. In each group, an observation network based on who observed whose task-solving behaviour was strongly correlated with networks based on affiliative interactions and proximity. Ravens with high social centrality (strength, eigenvector, information centrality) in the affiliative interaction network were also central in the observation network, possibly as a result of solving the task sooner. Network-based diffusion analysis revealed that the order that ravens first solved the task was best predicted by connections in the affiliative interaction network in a group of subadult ravens, and by social rank and kinship (which influenced affiliative interactions) in a group of juvenile ravens. Our results demonstrate that not all social connections are equally effective at predicting the patterns of selective observation and information transmission.

  6. Competition between Homophily and Information Entropy Maximization in Social Networks.

    PubMed

    Zhao, Jichang; Liang, Xiao; Xu, Ke

    2015-01-01

    In social networks, it is conventionally thought that two individuals with more overlapped friends tend to establish a new friendship, which could be stated as homophily breeding new connections. While the recent hypothesis of maximum information entropy is presented as the possible origin of effective navigation in small-world networks. We find there exists a competition between information entropy maximization and homophily in local structure through both theoretical and experimental analysis. This competition suggests that a newly built relationship between two individuals with more common friends would lead to less information entropy gain for them. We demonstrate that in the evolution of the social network, both of the two assumptions coexist. The rule of maximum information entropy produces weak ties in the network, while the law of homophily makes the network highly clustered locally and the individuals would obtain strong and trust ties. A toy model is also presented to demonstrate the competition and evaluate the roles of different rules in the evolution of real networks. Our findings could shed light on the social network modeling from a new perspective.

  7. Social networks predict selective observation and information spread in ravens.

    PubMed

    Kulahci, Ipek G; Rubenstein, Daniel I; Bugnyar, Thomas; Hoppitt, William; Mikus, Nace; Schwab, Christine

    2016-07-01

    Animals are predicted to selectively observe and learn from the conspecifics with whom they share social connections. Yet, hardly anything is known about the role of different connections in observation and learning. To address the relationships between social connections, observation and learning, we investigated transmission of information in two raven (Corvus corax) groups. First, we quantified social connections in each group by constructing networks on affiliative interactions, aggressive interactions and proximity. We then seeded novel information by training one group member on a novel task and allowing others to observe. In each group, an observation network based on who observed whose task-solving behaviour was strongly correlated with networks based on affiliative interactions and proximity. Ravens with high social centrality (strength, eigenvector, information centrality) in the affiliative interaction network were also central in the observation network, possibly as a result of solving the task sooner. Network-based diffusion analysis revealed that the order that ravens first solved the task was best predicted by connections in the affiliative interaction network in a group of subadult ravens, and by social rank and kinship (which influenced affiliative interactions) in a group of juvenile ravens. Our results demonstrate that not all social connections are equally effective at predicting the patterns of selective observation and information transmission. PMID:27493780

  8. Social networks predict selective observation and information spread in ravens

    PubMed Central

    Rubenstein, Daniel I.; Bugnyar, Thomas; Hoppitt, William; Mikus, Nace; Schwab, Christine

    2016-01-01

    Animals are predicted to selectively observe and learn from the conspecifics with whom they share social connections. Yet, hardly anything is known about the role of different connections in observation and learning. To address the relationships between social connections, observation and learning, we investigated transmission of information in two raven (Corvus corax) groups. First, we quantified social connections in each group by constructing networks on affiliative interactions, aggressive interactions and proximity. We then seeded novel information by training one group member on a novel task and allowing others to observe. In each group, an observation network based on who observed whose task-solving behaviour was strongly correlated with networks based on affiliative interactions and proximity. Ravens with high social centrality (strength, eigenvector, information centrality) in the affiliative interaction network were also central in the observation network, possibly as a result of solving the task sooner. Network-based diffusion analysis revealed that the order that ravens first solved the task was best predicted by connections in the affiliative interaction network in a group of subadult ravens, and by social rank and kinship (which influenced affiliative interactions) in a group of juvenile ravens. Our results demonstrate that not all social connections are equally effective at predicting the patterns of selective observation and information transmission. PMID:27493780

  9. On a theory of stability for nonlinear stochastic chemical reaction networks

    SciTech Connect

    Smadbeck, Patrick; Kaznessis, Yiannis N.

    2015-05-14

    We present elements of a stability theory for small, stochastic, nonlinear chemical reaction networks. Steady state probability distributions are computed with zero-information (ZI) closure, a closure algorithm that solves chemical master equations of small arbitrary nonlinear reactions. Stochastic models can be linearized around the steady state with ZI-closure, and the eigenvalues of the Jacobian matrix can be readily computed. Eigenvalues govern the relaxation of fluctuation autocorrelation functions at steady state. Autocorrelation functions reveal the time scales of phenomena underlying the dynamics of nonlinear reaction networks. In accord with the fluctuation-dissipation theorem, these functions are found to be congruent to response functions to small perturbations. Significant differences are observed in the stability of nonlinear reacting systems between deterministic and stochastic modeling formalisms.

  10. Coordinated Economic Development and the Information Network.

    ERIC Educational Resources Information Center

    Easton, D. K.

    This is a discussion of some of the problems that the Advisory Organization for Gulf Industries (AOGI) will face when it undertakes (1) to organize both an information center (node) that will serve the information needs of the Gulf States of Iraq, Kuwait, Bahrain, Saudi Arabia, Qatar, the United Arab Emirates (UAE) and Oman; and (2) to compile an…

  11. Automated Physico-Chemical Cell Model Development through Information Theory

    SciTech Connect

    Peter J. Ortoleva

    2005-11-29

    The objective of this project was to develop predictive models of the chemical responses of microbial cells to variations in their surroundings. The application of these models is optimization of environmental remediation and energy-producing biotechnical processes.The principles on which our project is based are as follows: chemical thermodynamics and kinetics; automation of calibration through information theory; integration of multiplex data (e.g. cDNA microarrays, NMR, proteomics), cell modeling, and bifurcation theory to overcome cellular complexity; and the use of multiplex data and information theory to calibrate and run an incomplete model. In this report we review four papers summarizing key findings and a web-enabled, multiple module workflow we have implemented that consists of a set of interoperable systems biology computational modules.

  12. C. elegans locomotion analysis using algorithmic information theory.

    PubMed

    Skandari, Roghieh; Le Bihan, Nicolas; Manton, Jonathan H

    2015-01-01

    This article investigates the use of algorithmic information theory to analyse C. elegans datasets. The ability of complexity measures to detect similarity in animals' behaviours is demonstrated and their strengths are compared to methods such as histograms. Introduced quantities are illustrated on a couple of real two-dimensional C. elegans datasets to investigate the thermotaxis and chemotaxis behaviours.

  13. Information theory and phenomenology of multiple hadronic production

    SciTech Connect

    Wilk, G. ); Wlodarczyk, Z. )

    1991-02-01

    Multiple hadronic production is usually visualized as a two-step process: the formation of some well-defined intermediate objects such as strings or fireballs and their subsequent hadronization (decays). It is shown how information theory provides us with a model-independent tool in dealing with the hadronization step for which the most plausible distributions of hadrons are formed.

  14. Reviewing Theory and Research on Informal and Incidental Learning

    ERIC Educational Resources Information Center

    Marsick, Victoria J.; Watkins, Karen E.; Callahan, Mary Wilson; Volpe, Marie

    2006-01-01

    Leaders and employees of today's organizations typically assume increasing responsibility for their own and their organization's learning. Much of that learning is informal or incidental. This article reviews theory and research to update that model and identify future research challenges. Through this review, groundwork will be laid for…

  15. Evaluating hydrological model performance using information theory-based metrics

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The accuracy-based model performance metrics not necessarily reflect the qualitative correspondence between simulated and measured streamflow time series. The objective of this work was to use the information theory-based metrics to see whether they can be used as complementary tool for hydrologic m...

  16. How multiple social networks affect user awareness: The information diffusion process in multiplex networks

    NASA Astrophysics Data System (ADS)

    Li, Weihua; Tang, Shaoting; Fang, Wenyi; Guo, Quantong; Zhang, Xiao; Zheng, Zhiming

    2015-10-01

    The information diffusion process in single complex networks has been extensively studied, especially for modeling the spreading activities in online social networks. However, individuals usually use multiple social networks at the same time, and can share the information they have learned from one social network to another. This phenomenon gives rise to a new diffusion process on multiplex networks with more than one network layer. In this paper we account for this multiplex network spreading by proposing a model of information diffusion in two-layer multiplex networks. We develop a theoretical framework using bond percolation and cascading failure to describe the intralayer and interlayer diffusion. This allows us to obtain analytical solutions for the fraction of informed individuals as a function of transmissibility T and the interlayer transmission rate θ . Simulation results show that interaction between layers can greatly enhance the information diffusion process. And explosive diffusion can occur even if the transmissibility of the focal layer is under the critical threshold, due to interlayer transmission.

  17. An Information Theory Approach to Nonlinear, Nonequilibrium Thermodynamics

    PubMed Central

    Rogers, David M.; Beck, Thomas L.

    2012-01-01

    Using the problem of ion channel thermodynamics as an example, we illustrate the idea of building up complex thermodynamic models by successively adding physical information. We present a new formulation of information algebra that generalizes methods of both information theory and statistical mechanics. From this foundation we derive a theory for ion channel kinetics, identifying a nonequilibrium ‘process’ free energy functional in addition to the well-known integrated work functionals. The Gibbs-Maxwell relation for the free energy functional is a Green-Kubo relation, applicable arbitrarily far from equilibrium, that captures the effect of non-local and time-dependent behavior from transient thermal and mechanical driving forces. Comparing the physical significance of the Lagrange multipliers to the canonical ensemble suggests definitions of nonequilibrium ensembles at constant capacitance or inductance in addition to constant resistance. Our result is that statistical mechanical descriptions derived from a few primitive algebraic operations on information can be used to create experimentally-relevant and computable models. By construction, these models may use information from more detailed atomistic simulations. Two surprising consequences to be explored in further work are that (in)distinguishability factors are automatically predicted from the problem formulation and that a direct analogue of the second law for thermodynamic entropy production is found by considering information loss in stochastic processes. The information loss identifies a novel contribution from the instantaneous information entropy that ensures non-negative loss. PMID:22966210

  18. An Information Theory Approach to Nonlinear, Nonequilibrium Thermodynamics.

    PubMed

    Rogers, David M; Beck, Thomas L; Rempe, Susan B

    2011-10-01

    Using the problem of ion channel thermodynamics as an example, we illustrate the idea of building up complex thermodynamic models by successively adding physical information. We present a new formulation of information algebra that generalizes methods of both information theory and statistical mechanics. From this foundation we derive a theory for ion channel kinetics, identifying a nonequilibrium 'process' free energy functional in addition to the well-known integrated work functionals. The Gibbs-Maxwell relation for the free energy functional is a Green-Kubo relation, applicable arbitrarily far from equilibrium, that captures the effect of non-local and time-dependent behavior from transient thermal and mechanical driving forces. Comparing the physical significance of the Lagrange multipliers to the canonical ensemble suggests definitions of nonequilibrium ensembles at constant capacitance or inductance in addition to constant resistance. Our result is that statistical mechanical descriptions derived from a few primitive algebraic operations on information can be used to create experimentally-relevant and computable models. By construction, these models may use information from more detailed atomistic simulations. Two surprising consequences to be explored in further work are that (in)distinguishability factors are automatically predicted from the problem formulation and that a direct analogue of the second law for thermodynamic entropy production is found by considering information loss in stochastic processes. The information loss identifies a novel contribution from the instantaneous information entropy that ensures non-negative loss.

  19. Learning biological network using mutual information and conditional independence

    PubMed Central

    2010-01-01

    Background Biological networks offer us a new way to investigate the interactions among different components and address the biological system as a whole. In this paper, a reverse-phase protein microarray (RPPM) is used for the quantitative measurement of proteomic responses. Results To discover the signaling pathway responsive to RPPM, a new structure learning algorithm of Bayesian networks is developed based on mutual Information, conditional independence, and graph immorality. Trusted biology networks are thus predicted by the new approach. As an application example, we investigate signaling networks of ataxia telangiectasis mutation (ATM). The study was carried out at different time points under different dosages for cell lines with and without gene transfection. To validate the performance ofthe proposed algorithm, comparison experiments were also implemented using three well-known networks. From the experiment results, our approach produces more reliable networks with a relatively small number of wrong connection especially in mid-size networks. By using the proposed method, we predicted different networks for ATM under different doses of radiation treatment, and those networks were compared with results from eight different protein protein interaction (PPI) databases. Conclusions By using a new protein microarray technology in combination with a new computational framework, we demonstrate an application of the methodology to the study of biological networks of ATM cell lines under low dose ionization radiation. PMID:20438656

  20. Inferring influenza global transmission networks without complete phylogenetic information

    PubMed Central

    Aris-Brosou, Stéphane

    2014-01-01

    Influenza is one of the most severe respiratory infections affecting humans throughout the world, yet the dynamics of its global transmission network are still contentious. Here, I describe a novel combination of phylogenetics, time series, and graph theory to analyze 14.25 years of data stratified in space and in time, focusing on the main target of the human immune response, the hemagglutinin gene. While bypassing the complete phylogenetic inference of huge data sets, the method still extracts information suggesting that waves of genetic or of nucleotide diversity circulate continuously around the globe for subtypes that undergo sustained transmission over several seasons, such as H3N2 and pandemic H1N1/09, while diversity of prepandemic H1N1 viruses had until 2009 a noncontinuous transmission pattern consistent with a source/sink model. Irrespective of the shift in the structure of H1N1 diversity circulation with the emergence of the pandemic H1N1/09 strain, US prevalence peaks during the winter months when genetic diversity is at its lowest. This suggests that a dominant strain is generally responsible for epidemics and that monitoring genetic and/or nucleotide diversity in real time could provide public health agencies with an indirect estimate of prevalence. PMID:24665342

  1. Attachment-dissociation network: some thoughts about a modern complex theory.

    PubMed

    Bovensiepen, Gustav

    2006-06-01

    The paper revises the complex theory in the light of modern infant research, neurosciences and object relation theory. The author takes up Jean Knox's idea to understand complexes as analogies to the internal working models of attachment theory. The author proposes to understand complexes as dissociated sub-networks out of the network structure of the psyche; these sub-networks contain the internal working models, the characteristic affects and unconscious expectation phantasies. With this network model one can try to understand severe defensive organizations in some patients as a pathological organization of different complexes. This is illustrated by a clinical example. PMID:16712687

  2. Scalable Hierarchical Network Management System for Displaying Network Information in Three Dimensions

    NASA Technical Reports Server (NTRS)

    George, Jude (Inventor); Schlecht, Leslie (Inventor); McCabe, James D. (Inventor); LeKashman, John Jr. (Inventor)

    1998-01-01

    A network management system has SNMP agents distributed at one or more sites, an input output module at each site, and a server module located at a selected site for communicating with input output modules, each of which is configured for both SNMP and HNMP communications. The server module is configured exclusively for HNMP communications, and it communicates with each input output module according to the HNMP. Non-iconified, informationally complete views are provided of network elements to aid in network management.

  3. Latency-information theory and applications: Part I. On the discovery of the time dual for information theory

    NASA Astrophysics Data System (ADS)

    Feria, Erlan H.

    2008-04-01

    As part of research conducted on the design of an efficient clutter covariance processor for DARPA's knowledge aided sensor signal processing expert reasoning (KASSPER) program a time-dual for information theory was discovered and named latency theory, this theory is discussed in this first of a multi-paper series. While information theory addresses the design of communication systems, latency theory does the same for recognition systems. Recognition system is the name given to the time dual of a communication system. A recognition system uses prior-knowledge about a signal-processor's input to enable the sensing of its output by a processing-time limited sensor when the fastest possible signal-processor replacement cannot achieve this task. A processor-coder is the time dual of a source coder. While a source coder replaces a signal-source to yield a smaller sourced-space in binary digits (bits) units a processor coder replaces a signal-processor to yield a smaller processing-time in binary operators (bors) units. A sensor coder is the time dual of a channel coder. While a channel coder identifies the necessary overhead-knowledge for accurate communications a sensor coder identifies the necessary prior-knowledge for accurate recognitions. In the second of this multipaper series latency theory is successfully illustrated with real-world knowledge aided radar.

  4. Stimulus information stored in lasting active and hidden network states is destroyed by network bursts.

    PubMed

    Dranias, Mark R; Westover, M Brandon; Cash, Sidney; VanDongen, Antonius M J

    2015-01-01

    In both humans and animals brief synchronizing bursts of epileptiform activity known as interictal epileptiform discharges (IEDs) can, even in the absence of overt seizures, cause transient cognitive impairments (TCI) that include problems with perception or short-term memory. While no evidence from single units is available, it has been assumed that IEDs destroy information represented in neuronal networks. Cultured neuronal networks are a model for generic cortical microcircuits, and their spontaneous activity is characterized by the presence of synchronized network bursts (SNBs), which share a number of properties with IEDs, including the high degree of synchronization and their spontaneous occurrence in the absence of an external stimulus. As a model approach to understanding the processes underlying IEDs, optogenetic stimulation and multielectrode array (MEA) recordings of cultured neuronal networks were used to study whether stimulus information represented in these networks survives SNBs. When such networks are optically stimulated they encode and maintain stimulus information for as long as one second. Experiments involved recording the network response to a single stimulus and trials where two different stimuli were presented sequentially, akin to a paired pulse trial. We broke the sequential stimulus trials into encoding, delay and readout phases and found that regardless of which phase the SNB occurs, stimulus-specific information was impaired. SNBs were observed to increase the mean network firing rate, but this did not translate monotonically into increases in network entropy. It was found that the more excitable a network, the more stereotyped its response was during a network burst. These measurements speak to whether SNBs are capable of transmitting information in addition to blocking it. These results are consistent with previous reports and provide baseline predictions concerning the neural mechanisms by which IEDs might cause TCI.

  5. Unified-theory-of-reinforcement neural networks do not simulate the blocking effect.

    PubMed

    Calvin, Nicholas T; J McDowell, J

    2015-11-01

    For the last 20 years the unified theory of reinforcement (Donahoe et al., 1993) has been used to develop computer simulations to evaluate its plausibility as an account for behavior. The unified theory of reinforcement states that operant and respondent learning occurs via the same neural mechanisms. As part of a larger project to evaluate the operant behavior predicted by the theory, this project was the first replication of neural network models based on the unified theory of reinforcement. In the process of replicating these neural network models it became apparent that a previously published finding, namely, that the networks simulate the blocking phenomenon (Donahoe et al., 1993), was a misinterpretation of the data. We show that the apparent blocking produced by these networks is an artifact of the inability of these networks to generate the same conditioned response to multiple stimuli. The piecemeal approach to evaluate the unified theory of reinforcement via simulation is critiqued and alternatives are discussed. PMID:26319369

  6. Unified-theory-of-reinforcement neural networks do not simulate the blocking effect.

    PubMed

    Calvin, Nicholas T; J McDowell, J

    2015-11-01

    For the last 20 years the unified theory of reinforcement (Donahoe et al., 1993) has been used to develop computer simulations to evaluate its plausibility as an account for behavior. The unified theory of reinforcement states that operant and respondent learning occurs via the same neural mechanisms. As part of a larger project to evaluate the operant behavior predicted by the theory, this project was the first replication of neural network models based on the unified theory of reinforcement. In the process of replicating these neural network models it became apparent that a previously published finding, namely, that the networks simulate the blocking phenomenon (Donahoe et al., 1993), was a misinterpretation of the data. We show that the apparent blocking produced by these networks is an artifact of the inability of these networks to generate the same conditioned response to multiple stimuli. The piecemeal approach to evaluate the unified theory of reinforcement via simulation is critiqued and alternatives are discussed.

  7. Information dynamics in small-world Boolean networks.

    PubMed

    Lizier, Joseph T; Pritam, Siddharth; Prokopenko, Mikhail

    2011-01-01

    Small-world networks have been one of the most influential concepts in complex systems science, partly due to their prevalence in naturally occurring networks. It is often suggested that this prevalence is due to an inherent capability to store and transfer information efficiently. We perform an ensemble investigation of the computational capabilities of small-world networks as compared to ordered and random topologies. To generate dynamic behavior for this experiment, we imbue the nodes in these networks with random Boolean functions. We find that the ordered phase of the dynamics (low activity in dynamics) and topologies with low randomness are dominated by information storage, while the chaotic phase (high activity in dynamics) and topologies with high randomness are dominated by information transfer. Information storage and information transfer are somewhat balanced (crossed over) near the small-world regime, providing quantitative evidence that small-world networks do indeed have a propensity to combine comparably large information storage and transfer capacity. PMID:21762020

  8. Symmetric polynomials in information theory: Entropy and subentropy

    SciTech Connect

    Jozsa, Richard; Mitchison, Graeme

    2015-06-15

    Entropy and other fundamental quantities of information theory are customarily expressed and manipulated as functions of probabilities. Here we study the entropy H and subentropy Q as functions of the elementary symmetric polynomials in the probabilities and reveal a series of remarkable properties. Derivatives of all orders are shown to satisfy a complete monotonicity property. H and Q themselves become multivariate Bernstein functions and we derive the density functions of their Levy-Khintchine representations. We also show that H and Q are Pick functions in each symmetric polynomial variable separately. Furthermore, we see that H and the intrinsically quantum informational quantity Q become surprisingly closely related in functional form, suggesting a special significance for the symmetric polynomials in quantum information theory. Using the symmetric polynomials, we also derive a series of further properties of H and Q.

  9. Museum Information Networks. Museum Data Bank Research Report No. 5.

    ERIC Educational Resources Information Center

    Chenhall, Robert G.

    Several museum information processing systems are discussed in relation to their underlying assumptions regarding museum information and networking needs that have resulted in inadequate service to some kinds of museums, particularly historical museums. Rather than any of these systems, a modular approach is proposed that would allow each museum…

  10. Towards a National Biomedical Information Network for Nigeria.

    ERIC Educational Resources Information Center

    Belleh, Godfrey S.

    1978-01-01

    Recommends that Medical School libraries be equipped to organize and provide biomedical information services in their respective states or areas, as a basis for the development of a national library-based biomedical information network to support Nigeria's programs of medical education, research, and health care delivery at all levels. (VT)

  11. Accessibility and Integrity of Networked Information Collections. Background Paper.

    ERIC Educational Resources Information Center

    Lynch, Clifford A.

    This paper considers questions related to the integrity and accessibility of new electronic information resources. It begins with a review of recent developments in networked information resources and the tools to identify, navigate, and use such resources. An overview is then given of the issues involved in access and integrity questions. Links…

  12. Creating Possible Selves: Information Disclosure Behaviour on Social Networks

    ERIC Educational Resources Information Center

    Bronstein, Jenny

    2014-01-01

    Introduction: This study investigates the creation of alternative identities or possible selves on social networks by examining self-presentation and self-disclosure as elements of the information disclosure behaviour of Facebook users. Method. An online questionnaire was distributed amongst library and information science students at Bar-Ilan…

  13. Child Rights Information Network Newsletter, 2000-2002.

    ERIC Educational Resources Information Center

    Khan, Andrea, Ed.; Greenwood, Laura, Ed.

    These five newsletter issues communicate activities of the Child Rights Information Network (CRIN) and report on information resources and world-wide activities concerning children and child rights. The March 2000 issue focuses on children's right to education, assessing the matter form a range of differing perspectives, at international and…

  14. Development through Information Networks in the Asia-Pacific Region.

    ERIC Educational Resources Information Center

    Amarasuriya, Nimala R.

    1987-01-01

    Discusses the need for access to scientific and technical information to attain national development goals in the Asia Pacific region, and outlines the objectives and program areas of a regional information network established by Unesco. Problems with the current system and future needs are identified. (CLB)

  15. Integrating network structure and dynamic information for better routing strategy on scale-free networks

    NASA Astrophysics Data System (ADS)

    Tang, Xiao-Gai; Wong, Eric W. M.; Wu, Zhi-Xi

    2009-06-01

    We study information packet routing processes on scale-free networks by mimicking the Internet traffic delivery strategies. We incorporate both the global network structure information and local queuing information in the dynamic processes. We propose several new routing strategies to guide the packet routing. The performance of the routing strategies is measured by the average transit time of the packets as well as their dependence on the traffic amount. We find that the routing strategies which integrate both global network structure information and local dynamic information perform much better than the traditional shortest-path routing protocol which takes into account only the global topological information. Moreover, from comparative studies of these routing strategies, we observe that some of our proposed methods can decrease the average transit time of packets but the performance is closely dependent on the total amount of traffic while some other proposed methods can have good performance independent of the total amount of traffic with hyper-excellent average transit time of packets. Also, numerical results show that our proposed methods integrating network structure information and local dynamic information can work much better than the methods recently proposed in [S. Sreenivasan, R. Cohen, E. López, Z. Toroczkai, H.E. Stanley, Phys. Rev. E 75 (2007) 036105, Zhi-Xi Wu, Gang Peng, Eric W.M. Wong, Kai-Hau Yeung, J. Stat. Mech. (2008) P11002.], which only considered network structure information.

  16. Design of surface-water data networks for regional information

    USGS Publications Warehouse

    Moss, Marshall E.; Gilroy, E.J.; Tasker, Gary D.; Karlinger, M.R.

    1982-01-01

    This report describes a technique, Network Analysis of Regional Information (NARI), and the existing computer procedures that have been developed for the specification of the regional information-cost relation for several statistical parameters of streamflow. The measure of information used is the true standard error of estimate of a regional logarithmic regression. The cost is a function of the number of stations at which hydrologic data are collected and the number of years for which the data are collected. The technique can be used to obtain either (1) a minimum cost network that will attain a prespecified accuracy and reliability or (2) a network that maximizes information given a set of budgetary and time constraints.

  17. Diffusion processes of fragmentary information on scale-free networks

    NASA Astrophysics Data System (ADS)

    Li, Xun; Cao, Lang

    2016-05-01

    Compartmental models of diffusion over contact networks have proven representative of real-life propagation phenomena among interacting individuals. However, there is a broad class of collective spreading mechanisms departing from compartmental representations, including those for diffusive objects capable of fragmentation and transmission unnecessarily as a whole. Here, we consider a continuous-state susceptible-infected-susceptible (SIS) model as an ideal limit-case of diffusion processes of fragmentary information on networks, where individuals possess fractions of the information content and update them by selectively exchanging messages with partners in the vicinity. Specifically, we incorporate local information, such as neighbors' node degrees and carried contents, into the individual partner choice, and examine the roles of a variety of such strategies in the information diffusion process, both qualitatively and quantitatively. Our method provides an effective and flexible route of modulating continuous-state diffusion dynamics on networks and has potential in a wide array of practical applications.

  18. Enhancing topology adaptation in information-sharing social networks

    NASA Astrophysics Data System (ADS)

    Cimini, Giulio; Chen, Duanbing; Medo, Matúš; Lü, Linyuan; Zhang, Yi-Cheng; Zhou, Tao

    2012-04-01

    The advent of the Internet and World Wide Web has led to unprecedent growth of the information available. People usually face the information overload by following a limited number of sources which best fit their interests. It has thus become important to address issues like who gets followed and how to allow people to discover new and better information sources. In this paper we conduct an empirical analysis of different online social networking sites and draw inspiration from its results to present different source selection strategies in an adaptive model for social recommendation. We show that local search rules which enhance the typical topological features of real social communities give rise to network configurations that are globally optimal. These rules create networks which are effective in information diffusion and resemble structures resulting from real social systems.

  19. ("Un")Doing Standards in Education with Actor-Network Theory

    ERIC Educational Resources Information Center

    Fenwick, Tara J.

    2010-01-01

    Recent critiques have drawn important attention to the depoliticized consensus and empty promises embedded in network discourses of educational policy. While acceding this critique, this discussion argues that some forms of network analysis--specifically those adopting actor-network theory (ANT) approaches--actually offer useful theoretical…

  20. Entropy and information causality in general probabilistic theories

    NASA Astrophysics Data System (ADS)

    Barnum, Howard; Barrett, Jonathan; Orloff Clark, Lisa; Leifer, Matthew; Spekkens, Robert; Stepanik, Nicholas; Wilce, Alex; Wilke, Robin

    2010-03-01

    We investigate the concept of entropy in probabilistic theories more general than quantum mechanics, with particular reference to the notion of information causality (IC) recently proposed by Pawlowski et al (2009 arXiv:0905.2292). We consider two entropic quantities, which we term measurement and mixing entropy. In the context of classical and quantum theory, these coincide, being given by the Shannon and von Neumann entropies, respectively; in general, however, they are very different. In particular, while measurement entropy is easily seen to be concave, mixing entropy need not be. In fact, as we show, mixing entropy is not concave whenever the state space is a non-simplicial polytope. Thus, the condition that measurement and mixing entropies coincide is a strong constraint on possible theories. We call theories with this property monoentropic. Measurement entropy is subadditive, but not in general strongly subadditive. Equivalently, if we define the mutual information between two systems A and B by the usual formula I(A: B)=H(A)+H(B)-H(AB), where H denotes the measurement entropy and AB is a non-signaling composite of A and B, then it can happen that I(A:BC)theory in which measurement entropy is strongly subadditive, and also satisfies a version of the Holevo bound, is informationally causal, and on the other hand we observe that Popescu-Rohrlich boxes, which violate IC, also violate strong subadditivity. We also explore the interplay between measurement and mixing entropy and various natural conditions on theories that arise in quantum axiomatics.

  1. What Information Theory Says about Bounded Rational Best Response

    NASA Technical Reports Server (NTRS)

    Wolpert, David H.

    2005-01-01

    Probability Collectives (PC) provides the information-theoretic extension of conventional full-rationality game theory to bounded rational games. Here an explicit solution to the equations giving the bounded rationality equilibrium of a game is presented. Then PC is used to investigate games in which the players use bounded rational best-response strategies. Next it is shown that in the continuum-time limit, bounded rational best response games result in a variant of the replicator dynamics of evolutionary game theory. It is then shown that for team (shared-payoff) games, this variant of replicator dynamics is identical to Newton-Raphson iterative optimization of the shared utility function.

  2. Using complex networks towards information retrieval and diagnostics in multidimensional imaging

    PubMed Central

    Banerjee, Soumya Jyoti; Azharuddin, Mohammad; Sen, Debanjan; Savale, Smruti; Datta, Himadri; Dasgupta, Anjan Kr; Roy, Soumen

    2015-01-01

    We present a fresh and broad yet simple approach towards information retrieval in general and diagnostics in particular by applying the theory of complex networks on multidimensional, dynamic images. We demonstrate a successful use of our method with the time series generated from high content thermal imaging videos of patients suffering from the aqueous deficient dry eye (ADDE) disease. Remarkably, network analyses of thermal imaging time series of contact lens users and patients upon whom Laser-Assisted in situ Keratomileusis (Lasik) surgery has been conducted, exhibit pronounced similarity with results obtained from ADDE patients. We also propose a general framework for the transformation of multidimensional images to networks for futuristic biometry. Our approach is general and scalable to other fluctuation-based devices where network parameters derived from fluctuations, act as effective discriminators and diagnostic markers. PMID:26626047

  3. Using complex networks towards information retrieval and diagnostics in multidimensional imaging

    NASA Astrophysics Data System (ADS)

    Banerjee, Soumya Jyoti; Azharuddin, Mohammad; Sen, Debanjan; Savale, Smruti; Datta, Himadri; Dasgupta, Anjan Kr; Roy, Soumen

    2015-12-01

    We present a fresh and broad yet simple approach towards information retrieval in general and diagnostics in particular by applying the theory of complex networks on multidimensional, dynamic images. We demonstrate a successful use of our method with the time series generated from high content thermal imaging videos of patients suffering from the aqueous deficient dry eye (ADDE) disease. Remarkably, network analyses of thermal imaging time series of contact lens users and patients upon whom Laser-Assisted in situ Keratomileusis (Lasik) surgery has been conducted, exhibit pronounced similarity with results obtained from ADDE patients. We also propose a general framework for the transformation of multidimensional images to networks for futuristic biometry. Our approach is general and scalable to other fluctuation-based devices where network parameters derived from fluctuations, act as effective discriminators and diagnostic markers.

  4. Using complex networks towards information retrieval and diagnostics in multidimensional imaging.

    PubMed

    Banerjee, Soumya Jyoti; Azharuddin, Mohammad; Sen, Debanjan; Savale, Smruti; Datta, Himadri; Dasgupta, Anjan Kr; Roy, Soumen

    2015-01-01

    We present a fresh and broad yet simple approach towards information retrieval in general and diagnostics in particular by applying the theory of complex networks on multidimensional, dynamic images. We demonstrate a successful use of our method with the time series generated from high content thermal imaging videos of patients suffering from the aqueous deficient dry eye (ADDE) disease. Remarkably, network analyses of thermal imaging time series of contact lens users and patients upon whom Laser-Assisted in situ Keratomileusis (Lasik) surgery has been conducted, exhibit pronounced similarity with results obtained from ADDE patients. We also propose a general framework for the transformation of multidimensional images to networks for futuristic biometry. Our approach is general and scalable to other fluctuation-based devices where network parameters derived from fluctuations, act as effective discriminators and diagnostic markers.

  5. Agricultural Sciences Information Network Development Plan.

    ERIC Educational Resources Information Center

    Interuniversity Communications Council, Bethesda, MD.

    This report is the last in a series of papers prepared by EDUCOM (the Interuniversity Communications Council) whose aim was to develop a long-range plan for strengthening information communication and exchange among the libraries of the land-grant institutions and the National Agricultural Library (NAL). The role of EDUCOM was to substantiate the…

  6. Models, Entropy and Information of Temporal Social Networks

    NASA Astrophysics Data System (ADS)

    Zhao, Kun; Karsai, Márton; Bianconi, Ginestra

    Temporal social networks are characterized by heterogeneous duration of contacts, which can either follow a power-law distribution, such as in face-to-face interactions, or a Weibull distribution, such as in mobile-phone communication. Here we model the dynamics of face-to-face interaction and mobile phone communication by a reinforcement dynamics, which explains the data observed in these different types of social interactions. We quantify the information encoded in the dynamics of these networks by the entropy of temporal networks. Finally, we show evidence that human dynamics is able to modulate the information present in social network dynamics when it follows circadian rhythms and when it is interfacing with a new technology such as the mobile-phone communication technology.

  7. Ranking streamflow model performance based on Information theory metrics

    NASA Astrophysics Data System (ADS)

    Martinez, Gonzalo; Pachepsky, Yakov; Pan, Feng; Wagener, Thorsten; Nicholson, Thomas

    2016-04-01

    The accuracy-based model performance metrics not necessarily reflect the qualitative correspondence between simulated and measured streamflow time series. The objective of this work was to use the information theory-based metrics to see whether they can be used as complementary tool for hydrologic model evaluation and selection. We simulated 10-year streamflow time series in five watersheds located in Texas, North Carolina, Mississippi, and West Virginia. Eight model of different complexity were applied. The information-theory based metrics were obtained after representing the time series as strings of symbols where different symbols corresponded to different quantiles of the probability distribution of streamflow. The symbol alphabet was used. Three metrics were computed for those strings - mean information gain that measures the randomness of the signal, effective measure complexity that characterizes predictability and fluctuation complexity that characterizes the presence of a pattern in the signal. The observed streamflow time series has smaller information content and larger complexity metrics than the precipitation time series. Watersheds served as information filters and and streamflow time series were less random and more complex than the ones of precipitation. This is reflected the fact that the watershed acts as the information filter in the hydrologic conversion process from precipitation to streamflow. The Nash Sutcliffe efficiency metric increased as the complexity of models increased, but in many cases several model had this efficiency values not statistically significant from each other. In such cases, ranking models by the closeness of the information-theory based parameters in simulated and measured streamflow time series can provide an additional criterion for the evaluation of hydrologic model performance.

  8. Information theory approach to the Landers aftershock sequence

    NASA Astrophysics Data System (ADS)

    Jiménez, Abigail

    2015-07-01

    The study of seismicity is becoming increasingly important with recent disasters such as the Gorkha event in Nepal in 2015. Our models mostly depend on the information given by a seismic catalog, such as rates of events and magnitudes. It has also been shown that seismicity presents long-range correlations. Here, we think about how they should be introduced in our models. We divide the region into cells and represent their activity as a time series. We then calculate how much information one cell has about the others in a future time. We find that the higher information content is in each cell with itself. By representing the region as a complex network, we can see that the information between distant cells passes thorough hubs that correspond to the main events. So we conclude that long-range interactions should be introduced as the interaction with the mainshocks, not with other cells except, perhaps, in the nearest neighbourhood.

  9. Maximum information entropy: a foundation for ecological theory.

    PubMed

    Harte, John; Newman, Erica A

    2014-07-01

    The maximum information entropy (MaxEnt) principle is a successful method of statistical inference that has recently been applied to ecology. Here, we show how MaxEnt can accurately predict patterns such as species-area relationships (SARs) and abundance distributions in macroecology and be a foundation for ecological theory. We discuss the conceptual foundation of the principle, why it often produces accurate predictions of probability distributions in science despite not incorporating explicit mechanisms, and how mismatches between predictions and data can shed light on driving mechanisms in ecology. We also review possible future extensions of the maximum entropy theory of ecology (METE), a potentially important foundation for future developments in ecological theory.

  10. Scalable Networked Information Processing Environment (SNIPE)

    SciTech Connect

    Fagg, G.E.; Moore, K.; Dongarra, J.J. |; Geist, A.

    1997-11-01

    SNIPE is a metacomputing system that aims to provide a reliable, secure, fault tolerant environment for long term distributed computing applications and data stores across the global Internet. This system combines global naming and replication of both processing and data to support large scale information processing applications leading to better availability and reliability than currently available with typical cluster computing and/or distributed computer environments.

  11. Gravity effects on information filtering and network evolving.

    PubMed

    Liu, Jin-Hu; Zhang, Zi-Ke; Chen, Lingjiao; Liu, Chuang; Yang, Chengcheng; Wang, Xueqi

    2014-01-01

    In this paper, based on the gravity principle of classical physics, we propose a tunable gravity-based model, which considers tag usage pattern to weigh both the mass and distance of network nodes. We then apply this model in solving the problems of information filtering and network evolving. Experimental results on two real-world data sets, Del.icio.us and MovieLens, show that it can not only enhance the algorithmic performance, but can also better characterize the properties of real networks. This work may shed some light on the in-depth understanding of the effect of gravity model.

  12. Computer network access to scientific information systems for minority universities

    NASA Astrophysics Data System (ADS)

    Thomas, Valerie L.; Wakim, Nagi T.

    1993-08-01

    The evolution of computer networking technology has lead to the establishment of a massive networking infrastructure which interconnects various types of computing resources at many government, academic, and corporate institutions. A large segment of this infrastructure has been developed to facilitate information exchange and resource sharing within the scientific community. The National Aeronautics and Space Administration (NASA) supports both the development and the application of computer networks which provide its community with access to many valuable multi-disciplinary scientific information systems and on-line databases. Recognizing the need to extend the benefits of this advanced networking technology to the under-represented community, the National Space Science Data Center (NSSDC) in the Space Data and Computing Division at the Goddard Space Flight Center has developed the Minority University-Space Interdisciplinary Network (MU-SPIN) Program: a major networking and education initiative for Historically Black Colleges and Universities (HBCUs) and Minority Universities (MUs). In this paper, we will briefly explain the various components of the MU-SPIN Program while highlighting how, by providing access to scientific information systems and on-line data, it promotes a higher level of collaboration among faculty and students and NASA scientists.

  13. Should the model for risk-informed regulation be game theory rather than decision theory?

    PubMed

    Bier, Vicki M; Lin, Shi-Woei

    2013-02-01

    Risk analysts frequently view the regulation of risks as being largely a matter of decision theory. According to this view, risk analysis methods provide information on the likelihood and severity of various possible outcomes; this information should then be assessed using a decision-theoretic approach (such as cost/benefit analysis) to determine whether the risks are acceptable, and whether additional regulation is warranted. However, this view ignores the fact that in many industries (particularly industries that are technologically sophisticated and employ specialized risk and safety experts), risk analyses may be done by regulated firms, not by the regulator. Moreover, those firms may have more knowledge about the levels of safety at their own facilities than the regulator does. This creates a situation in which the regulated firm has both the opportunity-and often also the motive-to provide inaccurate (in particular, favorably biased) risk information to the regulator, and hence the regulator has reason to doubt the accuracy of the risk information provided by regulated parties. Researchers have argued that decision theory is capable of dealing with many such strategic interactions as well as game theory can. This is especially true in two-player, two-stage games in which the follower has a unique best strategy in response to the leader's strategy, as appears to be the case in the situation analyzed in this article. However, even in such cases, we agree with Cox that game-theoretic methods and concepts can still be useful. In particular, the tools of mechanism design, and especially the revelation principle, can simplify the analysis of such games because the revelation principle provides rigorous assurance that it is sufficient to analyze only games in which licensees truthfully report their risk levels, making the problem more manageable. Without that, it would generally be necessary to consider much more complicated forms of strategic behavior (including

  14. Oscillations and Filtering Networks Support Flexible Routing of Information

    PubMed Central

    Akam, Thomas; Kullmann, Dimitri M.

    2010-01-01

    Summary The mammalian brain exhibits profuse interregional connectivity. How information flow is rapidly and flexibly switched among connected areas remains poorly understood. Task-dependent changes in the power and interregion coherence of network oscillations suggest that such oscillations play a role in signal routing. We show that switching one of several convergent pathways from an asynchronous to an oscillatory state allows accurate selective transmission of population-coded information, which can be extracted even when other convergent pathways fire asynchronously at comparable rates. We further show that the band-pass filtering required to perform this information extraction can be implemented in a simple spiking network model with a single feed-forward interneuron layer. This constitutes a mechanism for flexible signal routing in neural circuits, which exploits sparsely synchronized network oscillations and temporal filtering by feed-forward inhibition. Video Abstract PMID:20670837

  15. [Research on Zhejiang blood information network and management system].

    PubMed

    Yan, Li-Xing; Xu, Yan; Meng, Zhong-Hua; Kong, Chang-Hong; Wang, Jian-Min; Jin, Zhen-Liang; Wu, Shi-Ding; Chen, Chang-Shui; Luo, Ling-Fei

    2007-02-01

    This research was aimed to develop the first level blood information centralized database and real time communication network at a province area in China. Multiple technology like local area network database separate operation, real time data concentration and distribution mechanism, allopatric backup, and optical fiber virtual private network (VPN) were used. As a result, the blood information centralized database and management system were successfully constructed, which covers all the Zhejiang province, and the real time exchange of blood data was realised. In conclusion, its implementation promote volunteer blood donation and ensure the blood safety in Zhejiang, especially strengthen the quick response to public health emergency. This project lays the first stone of centralized test and allotment among blood banks in Zhejiang, and can serve as a reference of contemporary blood bank information systems in China.

  16. Lattice gauge theory simulations in the quantum information era

    NASA Astrophysics Data System (ADS)

    Dalmonte, M.; Montangero, S.

    2016-07-01

    The many-body problem is ubiquitous in the theoretical description of physical phenomena, ranging from the behaviour of elementary particles to the physics of electrons in solids. Most of our understanding of many-body systems comes from analysing the symmetric properties of Hamiltonian and states: the most striking examples are gauge theories such as quantum electrodynamics, where a local symmetry strongly constrains the microscopic dynamics. The physics of such gauge theories is relevant for the understanding of a diverse set of systems, including frustrated quantum magnets and the collective dynamics of elementary particles within the standard model. In the last few years, several approaches have been put forward to tackle the complex dynamics of gauge theories using quantum information concepts. In particular, quantum simulation platforms have been put forward for the realisation of synthetic gauge theories, and novel classical simulation algorithms based on quantum information concepts have been formulated. In this review, we present an introduction to these approaches, illustrating the basics concepts and highlighting the connections between apparently very different fields, and report the recent developments in this new thriving field of research.

  17. Information theory, animal communication, and the search for extraterrestrial intelligence

    NASA Astrophysics Data System (ADS)

    Doyle, Laurance R.; McCowan, Brenda; Johnston, Simon; Hanser, Sean F.

    2011-02-01

    We present ongoing research in the application of information theory to animal communication systems with the goal of developing additional detectors and estimators for possible extraterrestrial intelligent signals. Regardless of the species, for intelligence (i.e., complex knowledge) to be transmitted certain rules of information theory must still be obeyed. We demonstrate some preliminary results of applying information theory to socially complex marine mammal species (bottlenose dolphins and humpback whales) as well as arboreal squirrel monkeys, because they almost exclusively rely on vocal signals for their communications, producing signals which can be readily characterized by signal analysis. Metrics such as Zipf's Law and higher-order information-entropic structure are emerging as indicators of the communicative complexity characteristic of an "intelligent message" content within these animals' signals, perhaps not surprising given these species' social complexity. In addition to human languages, for comparison we also apply these metrics to pulsar signals—perhaps (arguably) the most "organized" of stellar systems—as an example of astrophysical systems that would have to be distinguished from an extraterrestrial intelligence message by such information theoretic filters. We also look at a message transmitted from Earth (Arecibo Observatory) that contains a lot of meaning but little information in the mathematical sense we define it here. We conclude that the study of non-human communication systems on our own planet can make a valuable contribution to the detection of extraterrestrial intelligence by providing quantitative general measures of communicative complexity. Studying the complex communication systems of other intelligent species on our own planet may also be one of the best ways to deprovincialize our thinking about extraterrestrial communication systems in general.

  18. Interspecific social networks promote information transmission in wild songbirds.

    PubMed

    Farine, Damien R; Aplin, Lucy M; Sheldon, Ben C; Hoppitt, William

    2015-03-22

    Understanding the functional links between social structure and population processes is a central aim of evolutionary ecology. Multiple types of interactions can be represented by networks drawn for the same population, such as kinship, dominance or affiliative networks, but the relative importance of alternative networks in modulating population processes may not be clear. We illustrate this problem, and a solution, by developing a framework for testing the importance of different types of association in facilitating the transmission of information. We apply this framework to experimental data from wild songbirds that form mixed-species flocks, recording the arrival (patch discovery) of individuals to novel foraging sites. We tested whether intraspecific and interspecific social networks predicted the spread of information about novel food sites, and found that both contributed to transmission. The likelihood of acquiring information per unit of connection to knowledgeable individuals increased 22-fold for conspecifics, and 12-fold for heterospecifics. We also found that species varied in how much information they produced, suggesting that some species play a keystone role in winter foraging flocks. More generally, these analyses demonstrate that this method provides a powerful approach, using social networks to quantify the relative transmission rates across different social relationships.

  19. Interspecific social networks promote information transmission in wild songbirds.

    PubMed

    Farine, Damien R; Aplin, Lucy M; Sheldon, Ben C; Hoppitt, William

    2015-03-22

    Understanding the functional links between social structure and population processes is a central aim of evolutionary ecology. Multiple types of interactions can be represented by networks drawn for the same population, such as kinship, dominance or affiliative networks, but the relative importance of alternative networks in modulating population processes may not be clear. We illustrate this problem, and a solution, by developing a framework for testing the importance of different types of association in facilitating the transmission of information. We apply this framework to experimental data from wild songbirds that form mixed-species flocks, recording the arrival (patch discovery) of individuals to novel foraging sites. We tested whether intraspecific and interspecific social networks predicted the spread of information about novel food sites, and found that both contributed to transmission. The likelihood of acquiring information per unit of connection to knowledgeable individuals increased 22-fold for conspecifics, and 12-fold for heterospecifics. We also found that species varied in how much information they produced, suggesting that some species play a keystone role in winter foraging flocks. More generally, these analyses demonstrate that this method provides a powerful approach, using social networks to quantify the relative transmission rates across different social relationships. PMID:25673683

  20. Interspecific social networks promote information transmission in wild songbirds

    PubMed Central

    Farine, Damien R.; Aplin, Lucy M.; Sheldon, Ben C.; Hoppitt, William

    2015-01-01

    Understanding the functional links between social structure and population processes is a central aim of evolutionary ecology. Multiple types of interactions can be represented by networks drawn for the same population, such as kinship, dominance or affiliative networks, but the relative importance of alternative networks in modulating population processes may not be clear. We illustrate this problem, and a solution, by developing a framework for testing the importance of different types of association in facilitating the transmission of information. We apply this framework to experimental data from wild songbirds that form mixed-species flocks, recording the arrival (patch discovery) of individuals to novel foraging sites. We tested whether intraspecific and interspecific social networks predicted the spread of information about novel food sites, and found that both contributed to transmission. The likelihood of acquiring information per unit of connection to knowledgeable individuals increased 22-fold for conspecifics, and 12-fold for heterospecifics. We also found that species varied in how much information they produced, suggesting that some species play a keystone role in winter foraging flocks. More generally, these analyses demonstrate that this method provides a powerful approach, using social networks to quantify the relative transmission rates across different social relationships. PMID:25673683

  1. Dynamics of sensory thalamocortical synaptic networks during information processing states.

    PubMed

    Castro-Alamancos, Manuel A

    2004-11-01

    The thalamocortical network consists of the pathways that interconnect the thalamus and neocortex, including thalamic sensory afferents, corticothalamic and thalamocortical pathways. These pathways are essential to acquire, analyze, store and retrieve sensory information. However, sensory information processing mostly occurs during behavioral arousal, when activity in thalamus and neocortex consists of an electrographic sign of low amplitude fast activity, known as activation, which is caused by several neuromodulator systems that project to the thalamocortical network. Logically, in order to understand how the thalamocortical network processes sensory information it is essential to study its response properties during states of activation. This paper reviews the temporal and spatial response properties of synaptic pathways in the whisker thalamocortical network of rodents during activated states as compared to quiescent (non-activated) states. The evidence shows that these pathways are differentially regulated via the effects of neuromodulators as behavioral contingencies demand. Thus, during activated states, the temporal and spatial response properties of pathways in the thalamocortical network are transformed to allow the processing of sensory information.

  2. Generalised squeezing and information theory approach to quantum entanglement

    NASA Technical Reports Server (NTRS)

    Vourdas, A.

    1993-01-01

    It is shown that the usual one- and two-mode squeezing are based on reducible representations of the SU(1,1) group. Generalized squeezing is introduced with the use of different SU(1,1) rotations on each irreducible sector. Two-mode squeezing entangles the modes and information theory methods are used to study this entanglement. The entanglement of three modes is also studied with the use of the strong subadditivity property of the entropy.

  3. Algorithmic information theory and the hidden variable question

    NASA Technical Reports Server (NTRS)

    Fuchs, Christopher

    1992-01-01

    The admissibility of certain nonlocal hidden-variable theories are explained via information theory. Consider a pair of Stern-Gerlach devices with fixed nonparallel orientations that periodically perform spin measurements on identically prepared pairs of electrons in the singlet spin state. Suppose the outcomes are recorded as binary strings l and r (with l sub n and r sub n denoting their n-length prefixes). The hidden-variable theories considered here require that there exists a recursive function which may be used to transform l sub n into r sub n for any n. This note demonstrates that such a theory cannot reproduce all the statistical predictions of quantum mechanics. Specifically, consider an ensemble of outcome pairs (l,r). From the associated probability measure, the Shannon entropies H sub n and H bar sub n for strings l sub n and pairs (l sub n, r sub n) may be formed. It is shown that such a theory requires that the absolute value of H bar sub n - H sub n be bounded - contrasting the quantum mechanical prediction that it grow with n.

  4. Algorithmic information theory and the hidden variable question

    NASA Astrophysics Data System (ADS)

    Fuchs, Christopher

    1992-02-01

    The admissibility of certain nonlocal hidden-variable theories are explained via information theory. Consider a pair of Stern-Gerlach devices with fixed nonparallel orientations that periodically perform spin measurements on identically prepared pairs of electrons in the singlet spin state. Suppose the outcomes are recorded as binary strings l and r (with l sub n and r sub n denoting their n-length prefixes). The hidden-variable theories considered here require that there exists a recursive function which may be used to transform l sub n into r sub n for any n. This note demonstrates that such a theory cannot reproduce all the statistical predictions of quantum mechanics. Specifically, consider an ensemble of outcome pairs (l,r). From the associated probability measure, the Shannon entropies H sub n and H bar sub n for strings l sub n and pairs (l sub n, r sub n) may be formed. It is shown that such a theory requires that the absolute value of H bar sub n - H sub n be bounded - contrasting the quantum mechanical prediction that it grow with n.

  5. Networking for Teacher Learning: Toward a Theory of Effective Design.

    ERIC Educational Resources Information Center

    McDonald, Joseph P.; Klein, Emily J.

    2003-01-01

    Examines how teacher networks design for teacher learning, describing several dynamic tensions inherent in the designs of a sample of teacher networks and assessing the relationships of these tensions to teacher learning. The paper illustrates these design concepts with reference to the work of seven networks that aim to revamp teacher' knowledge…

  6. Game Theory Based Security in Wireless Body Area Network with Stackelberg Security Equilibrium.

    PubMed

    Somasundaram, M; Sivakumar, R

    2015-01-01

    Wireless Body Area Network (WBAN) is effectively used in healthcare to increase the value of the patient's life and also the value of healthcare services. The biosensor based approach in medical care system makes it difficult to respond to the patients with minimal response time. The medical care unit does not deploy the accessing of ubiquitous broadband connections full time and hence the level of security will not be high always. The security issue also arises in monitoring the user body function records. Most of the systems on the Wireless Body Area Network are not effective in facing the security deployment issues. To access the patient's information with higher security on WBAN, Game Theory with Stackelberg Security Equilibrium (GTSSE) is proposed in this paper. GTSSE mechanism takes all the players into account. The patients are monitored by placing the power position authority initially. The position authority in GTSSE is the organizer and all the other players react to the organizer decision. Based on our proposed approach, experiment has been conducted on factors such as security ratio based on patient's health information, system flexibility level, energy consumption rate, and information loss rate. Stackelberg Security considerably improves the strength of solution with higher security.

  7. Game Theory Based Security in Wireless Body Area Network with Stackelberg Security Equilibrium

    PubMed Central

    Somasundaram, M.; Sivakumar, R.

    2015-01-01

    Wireless Body Area Network (WBAN) is effectively used in healthcare to increase the value of the patient's life and also the value of healthcare services. The biosensor based approach in medical care system makes it difficult to respond to the patients with minimal response time. The medical care unit does not deploy the accessing of ubiquitous broadband connections full time and hence the level of security will not be high always. The security issue also arises in monitoring the user body function records. Most of the systems on the Wireless Body Area Network are not effective in facing the security deployment issues. To access the patient's information with higher security on WBAN, Game Theory with Stackelberg Security Equilibrium (GTSSE) is proposed in this paper. GTSSE mechanism takes all the players into account. The patients are monitored by placing the power position authority initially. The position authority in GTSSE is the organizer and all the other players react to the organizer decision. Based on our proposed approach, experiment has been conducted on factors such as security ratio based on patient's health information, system flexibility level, energy consumption rate, and information loss rate. Stackelberg Security considerably improves the strength of solution with higher security. PMID:26759829

  8. Incorporating rapid neocortical learning of new schema-consistent information into complementary learning systems theory.

    PubMed

    McClelland, James L

    2013-11-01

    The complementary learning systems theory of the roles of hippocampus and neocortex (McClelland, McNaughton, & O'Reilly, 1995) holds that the rapid integration of arbitrary new information into neocortical structures is avoided to prevent catastrophic interference with structured knowledge representations stored in synaptic connections among neocortical neurons. Recent studies (Tse et al., 2007, 2011) showed that neocortical circuits can rapidly acquire new associations that are consistent with prior knowledge. The findings challenge the complementary learning systems theory as previously presented. However, new simulations extending those reported in McClelland et al. (1995) show that new information that is consistent with knowledge previously acquired by a putatively cortexlike artificial neural network can be learned rapidly and without interfering with existing knowledge; it is when inconsistent new knowledge is acquired quickly that catastrophic interference ensues. Several important features of the findings of Tse et al. (2007, 2011) are captured in these simulations, indicating that the neural network model used in McClelland et al. has characteristics in common with neocortical learning mechanisms. An additional simulation generalizes beyond the network model previously used, showing how the rate of change of cortical connections can depend on prior knowledge in an arguably more biologically plausible network architecture. In sum, the findings of Tse et al. are fully consistent with the idea that hippocampus and neocortex are complementary learning systems. Taken together, these findings and the simulations reported here advance our knowledge by bringing out the role of consistency of new experience with existing knowledge and demonstrating that the rate of change of connections in real and artificial neural networks can be strongly prior-knowledge dependent.

  9. Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation

    PubMed Central

    Hu, Hongzhi; Mao, Huajuan; Hu, Xiaohua; Hu, Feng; Sun, Xuemin; Jing, Zaiping; Duan, Yunsuo

    2015-01-01

    Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency. PMID:26609303

  10. Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation.

    PubMed

    Hu, Hongzhi; Mao, Huajuan; Hu, Xiaohua; Hu, Feng; Sun, Xuemin; Jing, Zaiping; Duan, Yunsuo

    2015-01-01

    Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency. PMID:26609303

  11. Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation.

    PubMed

    Hu, Hongzhi; Mao, Huajuan; Hu, Xiaohua; Hu, Feng; Sun, Xuemin; Jing, Zaiping; Duan, Yunsuo

    2015-01-01

    Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency.

  12. Realizing the Potential of Information Resources: Information, Technology, and Services. Track 6: Networking and Telecommunications.

    ERIC Educational Resources Information Center

    CAUSE, Boulder, CO.

    Six papers and two abstracts of papers are presented from the 1995 CAUSE conference track on networking and telecommunications issues faced by managers of information technology at colleges and universities. The papers include: (1) "Looking to the Year 2000: Alternatives in Campus Data Networking" (Noam H. Artz and Daniel A. Updegrove), which…

  13. Telecommunications Information Network: A Model for On-Demand Transfer of Medical Information. Annual Report.

    ERIC Educational Resources Information Center

    Lorenzi, Nancy M.; And Others

    This report describes and evaluates the first year of a demonstration project to develop an on-demand telecommunications network linking four remote hospitals in southwestern Ohio to the University of Cincinnati Medical Center. The Telecommunications Information Network (TIN) is designed to allow health care professionals at those hospitals to…

  14. Measuring information flow in cellular networks by the systems biology method through microarray data.

    PubMed

    Chen, Bor-Sen; Li, Cheng-Wei

    2015-01-01

    In general, it is very difficult to measure the information flow in a cellular network directly. In this study, based on an information flow model and microarray data, we measured the information flow in cellular networks indirectly by using a systems biology method. First, we used a recursive least square parameter estimation algorithm to identify the system parameters of coupling signal transduction pathways and the cellular gene regulatory network (GRN). Then, based on the identified parameters and systems theory, we estimated the signal transductivities of the coupling signal transduction pathways from the extracellular signals to each downstream protein and the information transductivities of the GRN between transcription factors in response to environmental events. According to the proposed method, the information flow, which is characterized by signal transductivity in coupling signaling pathways and information transductivity in the GRN, can be estimated by microarray temporal data or microarray sample data. It can also be estimated by other high-throughput data such as next-generation sequencing or proteomic data. Finally, the information flows of the signal transduction pathways and the GRN in leukemia cancer cells and non-leukemia normal cells were also measured to analyze the systematic dysfunction in this cancer from microarray sample data. The results show that the signal transductivities of signal transduction pathways change substantially from normal cells to leukemia cancer cells.

  15. Information in a Network of Neuronal Cells: Effect of Cell Density and Short-Term Depression.

    PubMed

    Onesto, Valentina; Cosentino, Carlo; Di Fabrizio, Enzo; Cesarelli, Mario; Amato, Francesco; Gentile, Francesco

    2016-01-01

    Neurons are specialized, electrically excitable cells which use electrical to chemical signals to transmit and elaborate information. Understanding how the cooperation of a great many of neurons in a grid may modify and perhaps improve the information quality, in contrast to few neurons in isolation, is critical for the rational design of cell-materials interfaces for applications in regenerative medicine, tissue engineering, and personalized lab-on-a-chips. In the present paper, we couple an integrate-and-fire model with information theory variables to analyse the extent of information in a network of nerve cells. We provide an estimate of the information in the network in bits as a function of cell density and short-term depression time. In the model, neurons are connected through a Delaunay triangulation of not-intersecting edges; in doing so, the number of connecting synapses per neuron is approximately constant to reproduce the early time of network development in planar neural cell cultures. In simulations where the number of nodes is varied, we observe an optimal value of cell density for which information in the grid is maximized. In simulations in which the posttransmission latency time is varied, we observe that information increases as the latency time decreases and, for specific configurations of the grid, it is largely enhanced in a resonance effect. PMID:27403421

  16. Information in a Network of Neuronal Cells: Effect of Cell Density and Short-Term Depression

    PubMed Central

    Onesto, Valentina; Cosentino, Carlo; Di Fabrizio, Enzo; Cesarelli, Mario; Amato, Francesco; Gentile, Francesco

    2016-01-01

    Neurons are specialized, electrically excitable cells which use electrical to chemical signals to transmit and elaborate information. Understanding how the cooperation of a great many of neurons in a grid may modify and perhaps improve the information quality, in contrast to few neurons in isolation, is critical for the rational design of cell-materials interfaces for applications in regenerative medicine, tissue engineering, and personalized lab-on-a-chips. In the present paper, we couple an integrate-and-fire model with information theory variables to analyse the extent of information in a network of nerve cells. We provide an estimate of the information in the network in bits as a function of cell density and short-term depression time. In the model, neurons are connected through a Delaunay triangulation of not-intersecting edges; in doing so, the number of connecting synapses per neuron is approximately constant to reproduce the early time of network development in planar neural cell cultures. In simulations where the number of nodes is varied, we observe an optimal value of cell density for which information in the grid is maximized. In simulations in which the posttransmission latency time is varied, we observe that information increases as the latency time decreases and, for specific configurations of the grid, it is largely enhanced in a resonance effect. PMID:27403421

  17. Enabling information management systems in tactical network environments

    NASA Astrophysics Data System (ADS)

    Carvalho, Marco; Uszok, Andrzej; Suri, Niranjan; Bradshaw, Jeffrey M.; Ceccio, Philip J.; Hanna, James P.; Sinclair, Asher

    2009-05-01

    Net-Centric Information Management (IM) and sharing in tactical environments promises to revolutionize forward command and control capabilities by providing ubiquitous shared situational awareness to the warfighter. This vision can be realized by leveraging the tactical and Mobile Ad hoc Networks (MANET) which provide the underlying communications infrastructure, but, significant technical challenges remain. Enabling information management in these highly dynamic environments will require multiple support services and protocols which are affected by, and highly dependent on, the underlying capabilities and dynamics of the tactical network infrastructure. In this paper we investigate, discuss, and evaluate the effects of realistic tactical and mobile communications network environments on mission-critical information management systems. We motivate our discussion by introducing the Advanced Information Management System (AIMS) which is targeted for deployment in tactical sensor systems. We present some operational requirements for AIMS and highlight how critical IM support services such as discovery, transport, federation, and Quality of Service (QoS) management are necessary to meet these requirements. Our goal is to provide a qualitative analysis of the impact of underlying assumptions of availability and performance of some of the critical services supporting tactical information management. We will also propose and describe a number of technologies and capabilities that have been developed to address these challenges, providing alternative approaches for transport, service discovery, and federation services for tactical networks.

  18. Temporal Evolution Of Information In Neural Networks With Feedback

    NASA Astrophysics Data System (ADS)

    Giahi Saravani, Aram; Pitkow, Xaq

    2015-03-01

    Recurrent neural networks are pivotal for information processing in the brain. Here we analyze how the information content of a neural population is altered by dynamic feedback of a stimulus estimated from the network activity. We find that the temporal evolution of the Fisher information in the model with feedback is bounded by the Fisher information in a network of pure integrators. The available information in the feedback model saturates with a time constant and to a final level both determined by the match between the estimator weights and the feedback weights. This network then encodes signals specifically from either the beginning or the end of the stimulus presentation, depending on this match. These results are relevant to recent experimental measurements of psychophysical kernels indicating that earlier stimuli have a stronger influence on perceptual discriminations than more recent stimuli. We discuss consequences of this description for choice correlations, a measure of how individual neuronal responses relate to perceptual estimates. McNair Foundation, Baylor College of Medicine, Rice University.

  19. Information dynamics of brain-heart physiological networks during sleep

    NASA Astrophysics Data System (ADS)

    Faes, L.; Nollo, G.; Jurysta, F.; Marinazzo, D.

    2014-10-01

    This study proposes an integrated approach, framed in the emerging fields of network physiology and information dynamics, for the quantitative analysis of brain-heart interaction networks during sleep. With this approach, the time series of cardiac vagal autonomic activity and brain wave activities measured respectively as the normalized high frequency component of heart rate variability and the EEG power in the δ, θ, α, σ, and β bands, are considered as realizations of the stochastic processes describing the dynamics of the heart system and of different brain sub-systems. Entropy-based measures are exploited to quantify the predictive information carried by each (sub)system, and to dissect this information into a part actively stored in the system and a part transferred to it from the other connected systems. The application of this approach to polysomnographic recordings of ten healthy subjects led us to identify a structured network of sleep brain-brain and brain-heart interactions, with the node described by the β EEG power acting as a hub which conveys the largest amount of information flowing between the heart and brain nodes. This network was found to be sustained mostly by the transitions across different sleep stages, as the information transfer was weaker during specific stages than during the whole night, and vanished progressively when moving from light sleep to deep sleep and to REM sleep.

  20. Self-Organized Information Processing in Neuronal Networks: Replacing Layers in Deep Networks by Dynamics

    NASA Astrophysics Data System (ADS)

    Kirst, Christoph

    It is astonishing how the sub-parts of a brain co-act to produce coherent behavior. What are mechanism that coordinate information processing and communication and how can those be changed flexibly in order to cope with variable contexts? Here we show that when information is encoded in the deviations around a collective dynamical reference state of a recurrent network the propagation of these fluctuations is strongly dependent on precisely this underlying reference. Information here 'surfs' on top of the collective dynamics and switching between states enables fast and flexible rerouting of information. This in turn affects local processing and consequently changes in the global reference dynamics that re-regulate the distribution of information. This provides a generic mechanism for self-organized information processing as we demonstrate with an oscillatory Hopfield network that performs contextual pattern recognition. Deep neural networks have proven to be very successful recently. Here we show that generating information channels via collective reference dynamics can effectively compress a deep multi-layer architecture into a single layer making this mechanism a promising candidate for the organization of information processing in biological neuronal networks.

  1. Suppressing disease spreading by using information diffusion on multiplex networks

    NASA Astrophysics Data System (ADS)

    Wang, Wei; Liu, Quan-Hui; Cai, Shi-Min; Tang, Ming; Braunstein, Lidia A.; Stanley, H. Eugene

    2016-07-01

    Although there is always an interplay between the dynamics of information diffusion and disease spreading, the empirical research on the systemic coevolution mechanisms connecting these two spreading dynamics is still lacking. Here we investigate the coevolution mechanisms and dynamics between information and disease spreading by utilizing real data and a proposed spreading model on multiplex network. Our empirical analysis finds asymmetrical interactions between the information and disease spreading dynamics. Our results obtained from both the theoretical framework and extensive stochastic numerical simulations suggest that an information outbreak can be triggered in a communication network by its own spreading dynamics or by a disease outbreak on a contact network, but that the disease threshold is not affected by information spreading. Our key finding is that there is an optimal information transmission rate that markedly suppresses the disease spreading. We find that the time evolution of the dynamics in the proposed model qualitatively agrees with the real-world spreading processes at the optimal information transmission rate.

  2. Suppressing disease spreading by using information diffusion on multiplex networks.

    PubMed

    Wang, Wei; Liu, Quan-Hui; Cai, Shi-Min; Tang, Ming; Braunstein, Lidia A; Stanley, H Eugene

    2016-01-01

    Although there is always an interplay between the dynamics of information diffusion and disease spreading, the empirical research on the systemic coevolution mechanisms connecting these two spreading dynamics is still lacking. Here we investigate the coevolution mechanisms and dynamics between information and disease spreading by utilizing real data and a proposed spreading model on multiplex network. Our empirical analysis finds asymmetrical interactions between the information and disease spreading dynamics. Our results obtained from both the theoretical framework and extensive stochastic numerical simulations suggest that an information outbreak can be triggered in a communication network by its own spreading dynamics or by a disease outbreak on a contact network, but that the disease threshold is not affected by information spreading. Our key finding is that there is an optimal information transmission rate that markedly suppresses the disease spreading. We find that the time evolution of the dynamics in the proposed model qualitatively agrees with the real-world spreading processes at the optimal information transmission rate. PMID:27380881

  3. Suppressing disease spreading by using information diffusion on multiplex networks

    PubMed Central

    Wang, Wei; Liu, Quan-Hui; Cai, Shi-Min; Tang, Ming; Braunstein, Lidia A.; Stanley, H. Eugene

    2016-01-01

    Although there is always an interplay between the dynamics of information diffusion and disease spreading, the empirical research on the systemic coevolution mechanisms connecting these two spreading dynamics is still lacking. Here we investigate the coevolution mechanisms and dynamics between information and disease spreading by utilizing real data and a proposed spreading model on multiplex network. Our empirical analysis finds asymmetrical interactions between the information and disease spreading dynamics. Our results obtained from both the theoretical framework and extensive stochastic numerical simulations suggest that an information outbreak can be triggered in a communication network by its own spreading dynamics or by a disease outbreak on a contact network, but that the disease threshold is not affected by information spreading. Our key finding is that there is an optimal information transmission rate that markedly suppresses the disease spreading. We find that the time evolution of the dynamics in the proposed model qualitatively agrees with the real-world spreading processes at the optimal information transmission rate. PMID:27380881

  4. Suppressing disease spreading by using information diffusion on multiplex networks.

    PubMed

    Wang, Wei; Liu, Quan-Hui; Cai, Shi-Min; Tang, Ming; Braunstein, Lidia A; Stanley, H Eugene

    2016-01-01

    Although there is always an interplay between the dynamics of information diffusion and disease spreading, the empirical research on the systemic coevolution mechanisms connecting these two spreading dynamics is still lacking. Here we investigate the coevolution mechanisms and dynamics between information and disease spreading by utilizing real data and a proposed spreading model on multiplex network. Our empirical analysis finds asymmetrical interactions between the information and disease spreading dynamics. Our results obtained from both the theoretical framework and extensive stochastic numerical simulations suggest that an information outbreak can be triggered in a communication network by its own spreading dynamics or by a disease outbreak on a contact network, but that the disease threshold is not affected by information spreading. Our key finding is that there is an optimal information transmission rate that markedly suppresses the disease spreading. We find that the time evolution of the dynamics in the proposed model qualitatively agrees with the real-world spreading processes at the optimal information transmission rate.

  5. Running a network on a shoestring: the Global Invasive Species Information Network

    USGS Publications Warehouse

    Jarnevich, Catherine S.; Simpson, Annie; Graham, James J; Newman, Gregory J.; Bargeron, Chuck T.

    2015-01-01

    The Global Invasive Species Information Network (GISIN) was conceptualized in 2004 to aggregate and disseminate invasive species data in a standardized way. A decade later the GISIN community has implemented a data portal and three of six GISIN data aggregation models in the GISIN data exchange Protocol, including invasive species status information, resource URLs, and occurrence data. The portal is based on a protocol developed by representatives from 15 countries and 27 organizations of the global invasive species information management community. The GISIN has 19 data providers sharing 34,343 species status records, 1,693,073 occurrences, and 15,601 resource URLs. While the GISIN's goal is to be global, much of its data and funding are provided by the United States. Several initiatives use the GISIN as their information backbone, such as the Great Lakes Early Detection Network (GLEDN) and the North American Invasive Species Network (NAISN). Here we share several success stories and organizational challenges that remain.

  6. Probing models of information spreading in social networks

    NASA Astrophysics Data System (ADS)

    Zoller, J.; Montangero, S.

    2014-10-01

    We apply signal processing analysis to the information spreading in a scale-free network. To reproduce typical behaviours obtained from the analysis of information spreading in the World Wide Web, we use a modified SIS (from ‘susceptible-infectious-susceptible’) model where synergy effects and influential nodes are taken into account. This model depends on a single free parameter that characterizes the memory time of the spreading process. We show that by means of fractal analysis it is possible—from aggregated easily accessible data—to gain information on the memory time of the underlying mechanism driving the information spreading process.

  7. Extraction of business relationships in supply networks using statistical learning theory.

    PubMed

    Zuo, Yi; Kajikawa, Yuya; Mori, Junichiro

    2016-06-01

    Supply chain management represents one of the most important scientific streams of operations research. The supply of energy, materials, products, and services involves millions of transactions conducted among national and local business enterprises. To deliver efficient and effective support for supply chain design and management, structural analyses and predictive models of customer-supplier relationships are expected to clarify current enterprise business conditions and to help enterprises identify innovative business partners for future success. This article presents the outcomes of a recent structural investigation concerning a supply network in the central area of Japan. We investigated the effectiveness of statistical learning theory to express the individual differences of a supply chain of enterprises within a certain business community using social network analysis. In the experiments, we employ support vector machine to train a customer-supplier relationship model on one of the main communities extracted from a supply network in the central area of Japan. The prediction results reveal an F-value of approximately 70% when the model is built by using network-based features, and an F-value of approximately 77% when the model is built by using attribute-based features. When we build the model based on both, F-values are improved to approximately 82%. The results of this research can help to dispel the implicit design space concerning customer-supplier relationships, which can be explored and refined from detailed topological information provided by network structures rather than from traditional and attribute-related enterprise profiles. We also investigate and discuss differences in the predictive accuracy of the model for different sizes of enterprises and types of business communities. PMID:27441294

  8. Extraction of business relationships in supply networks using statistical learning theory.

    PubMed

    Zuo, Yi; Kajikawa, Yuya; Mori, Junichiro

    2016-06-01

    Supply chain management represents one of the most important scientific streams of operations research. The supply of energy, materials, products, and services involves millions of transactions conducted among national and local business enterprises. To deliver efficient and effective support for supply chain design and management, structural analyses and predictive models of customer-supplier relationships are expected to clarify current enterprise business conditions and to help enterprises identify innovative business partners for future success. This article presents the outcomes of a recent structural investigation concerning a supply network in the central area of Japan. We investigated the effectiveness of statistical learning theory to express the individual differences of a supply chain of enterprises within a certain business community using social network analysis. In the experiments, we employ support vector machine to train a customer-supplier relationship model on one of the main communities extracted from a supply network in the central area of Japan. The prediction results reveal an F-value of approximately 70% when the model is built by using network-based features, and an F-value of approximately 77% when the model is built by using attribute-based features. When we build the model based on both, F-values are improved to approximately 82%. The results of this research can help to dispel the implicit design space concerning customer-supplier relationships, which can be explored and refined from detailed topological information provided by network structures rather than from traditional and attribute-related enterprise profiles. We also investigate and discuss differences in the predictive accuracy of the model for different sizes of enterprises and types of business communities.

  9. A game theory approach to target tracking in sensor networks.

    PubMed

    Gu, Dongbing

    2011-02-01

    In this paper, we investigate a moving-target tracking problem with sensor networks. Each sensor node has a sensor to observe the target and a processor to estimate the target position. It also has wireless communication capability but with limited range and can only communicate with neighbors. The moving target is assumed to be an intelligent agent, which is "smart" enough to escape from the detection by maximizing the estimation error. This adversary behavior makes the target tracking problem more difficult. We formulate this target estimation problem as a zero-sum game in this paper and use a minimax filter to estimate the target position. The minimax filter is a robust filter that minimizes the estimation error by considering the worst case noise. Furthermore, we develop a distributed version of the minimax filter for multiple sensor nodes. The distributed computation is implemented via modeling the information received from neighbors as measurements in the minimax filter. The simulation results show that the target tracking algorithm proposed in this paper provides a satisfactory result. PMID:20194057

  10. Information Spread of Emergency Events: Path Searching on Social Networks

    PubMed Central

    Hu, Hongzhi; Wu, Tunan

    2014-01-01

    Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning. PMID:24600323

  11. Information spread of emergency events: path searching on social networks.

    PubMed

    Dai, Weihui; Hu, Hongzhi; Wu, Tunan; Dai, Yonghui

    2014-01-01

    Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning.

  12. Dynamical coding of sensory information with competitive networks.

    PubMed

    Rabinovich, M I; Huerta, R; Volkovskii, A; Abarbanel, H D; Stopfer, M; Laurent, G

    2000-01-01

    Based on experiments with the locust olfactory system, we demonstrate that model sensory neural networks with lateral inhibition can generate stimulus specific identity-temporal patterns in the form of stimulus-dependent switching among small and dynamically changing neural ensembles (each ensemble being a group of synchronized projection neurons). Networks produce this switching mode of dynamical activity when lateral inhibitory connections are strongly non-symmetric. Such coding uses 'winner-less competitive' (WLC) dynamics. In contrast to the well known winner-take-all competitive (WTA) networks and Hopfield nets, winner-less competition represents sensory information dynamically. Such dynamics are reproducible, robust against intrinsic noise and sensitive to changes in the sensory input. We demonstrate the validity of sensory coding with WLC networks using two different formulations of the dynamics, namely the average and spiking dynamics of projection neurons (PN).

  13. Chaotic, informational and synchronous behaviour of multiplex networks

    PubMed Central

    Baptista, M. S.; Szmoski, R. M.; Pereira, R. F.; Pinto, S. E. de Souza

    2016-01-01

    The understanding of the relationship between topology and behaviour in interconnected networks would allow to charac- terise and predict behaviour in many real complex networks since both are usually not simultaneously known. Most previous studies have focused on the relationship between topology and synchronisation. In this work, we provide analytical formulas that shows how topology drives complex behaviour: chaos, information, and weak or strong synchronisation; in multiplex net- works with constant Jacobian. We also study this relationship numerically in multiplex networks of Hindmarsh-Rose neurons. Whereas behaviour in the analytically tractable network is a direct but not trivial consequence of the spectra of eigenvalues of the Laplacian matrix, where behaviour may strongly depend on the break of symmetry in the topology of interconnections, in Hindmarsh-Rose neural networks the nonlinear nature of the chemical synapses breaks the elegant mathematical connec- tion between the spectra of eigenvalues of the Laplacian matrix and the behaviour of the network, creating networks whose behaviour strongly depends on the nature (chemical or electrical) of the inter synapses. PMID:26939580

  14. Intellectual property and networked health information: issues and principles.

    PubMed Central

    Cate, F H

    1996-01-01

    Information networks offer enormous potential for improving the delivery of health care services, facilitating health-related decision-making, and contributing to better health. In addition, advanced information technologies offer important opportunities for new markets, targeted information products and services, greater accessibility, lower costs and prices, and more rapid and efficient distribution. Realizing the full potential of those information resources requires the resolution of significant intellectual property issues, some of which may be affected by special features of health information. For example, the government is a significant funder and originator of health-related information. In addition, much of that information is of great importance to the population and benefits not only individual users, but also employers, insurance companies, the government, and society as a whole. The government must therefore continue to provide particularly important health information to the public, and facilitate that information's accessibility and reliability, while avoiding unnecessary competition with private information providers. Congress and courts must modify or interpret current copyright law as necessary to guarantee that it does not interfere with innovation in tailored health information or exceed its constitutional boundaries and restrict access to information, as opposed to expression. Both producers and users of information must work with the government to educate the public about the availability of health information and the rights of and limitations upon users under copyright law. PMID:8826629

  15. Frames, designs, and spherical codes in quantum information theory

    NASA Astrophysics Data System (ADS)

    Renes, Joseph M.

    Frame theory offers a lens through which to view a large portion of quantum information theory, providing an organizational principle to those topics in its purview. In this thesis, I cut a trail from foundational questions to practical applications, from the origin of the quantum probability rule to quantum cryptography, by way of a standard quantum measurement helpful in quantum tomography and representation of quantum theory. Before embarking, preparations are undertaken by outlining the relevant aspects of frame theory, particularly the characterization of generalized orthonormal bases in terms of physical quantum measurements, as well as several aesthetically appealing families of measurements, each possessing a high degree of symmetry. Much more than just elegant, though, these quantum measurements are found to be useful in many aspects of quantum information theory. I first consider the foundational question of justifying the quantum probability rule, showing that putting a probability valuation on generalized quantum measurements leads directly to the Born rule. Moreover, for qubits, the case neglected in the traditional formulation of Gleason's theorem, a symmetric three-outcome measurement called the trine is sufficient to impel the desired form. Keeping with foundational questions, I then turn to the problem of establishing a symmetric measurement capable of effortlessly rendering quantum theory in terms of classical probability theory. Numerical results provide an almost utterly convincing amount of evidence for this, justifying the subsequent study of its use in quantum tomography and detailed account of the properties of the reduction to probabilistic terms. Saving perhaps the most exciting topic for last, I make use of these aesthetic ensembles in the applied field of quantum cryptography. A large class of streamlined key distribution protocols may be cut from the cloth of these ensembles, and their symmetry affords them improved tolerance to

  16. Topology for efficient information dissemination in ad-hoc networking

    NASA Technical Reports Server (NTRS)

    Jennings, E.; Okino, C. M.

    2002-01-01

    In this paper, we explore the information dissemination problem in ad-hoc wirless networks. First, we analyze the probability of successful broadcast, assuming: the nodes are uniformly distributed, the available area has a lower bould relative to the total number of nodes, and there is zero knowledge of the overall topology of the network. By showing that the probability of such events is small, we are motivated to extract good graph topologies to minimize the overall transmissions. Three algorithms are used to generate topologies of the network with guaranteed connectivity. These are the minimum radius graph, the relative neighborhood graph and the minimum spanning tree. Our simulation shows that the relative neighborhood graph has certain good graph properties, which makes it suitable for efficient information dissemination.

  17. Role of information theoretic uncertainty relations in quantum theory

    SciTech Connect

    Jizba, Petr; Dunningham, Jacob A.; Joo, Jaewoo

    2015-04-15

    Uncertainty relations based on information theory for both discrete and continuous distribution functions are briefly reviewed. We extend these results to account for (differential) Rényi entropy and its related entropy power. This allows us to find a new class of information-theoretic uncertainty relations (ITURs). The potency of such uncertainty relations in quantum mechanics is illustrated with a simple two-energy-level model where they outperform both the usual Robertson–Schrödinger uncertainty relation and Shannon entropy based uncertainty relation. In the continuous case the ensuing entropy power uncertainty relations are discussed in the context of heavy tailed wave functions and Schrödinger cat states. Again, improvement over both the Robertson–Schrödinger uncertainty principle and Shannon ITUR is demonstrated in these cases. Further salient issues such as the proof of a generalized entropy power inequality and a geometric picture of information-theoretic uncertainty relations are also discussed.

  18. Role of information theoretic uncertainty relations in quantum theory

    NASA Astrophysics Data System (ADS)

    Jizba, Petr; Dunningham, Jacob A.; Joo, Jaewoo

    2015-04-01

    Uncertainty relations based on information theory for both discrete and continuous distribution functions are briefly reviewed. We extend these results to account for (differential) Rényi entropy and its related entropy power. This allows us to find a new class of information-theoretic uncertainty relations (ITURs). The potency of such uncertainty relations in quantum mechanics is illustrated with a simple two-energy-level model where they outperform both the usual Robertson-Schrödinger uncertainty relation and Shannon entropy based uncertainty relation. In the continuous case the ensuing entropy power uncertainty relations are discussed in the context of heavy tailed wave functions and Schrödinger cat states. Again, improvement over both the Robertson-Schrödinger uncertainty principle and Shannon ITUR is demonstrated in these cases. Further salient issues such as the proof of a generalized entropy power inequality and a geometric picture of information-theoretic uncertainty relations are also discussed.

  19. Dysregulation between emotion and theory of mind networks in borderline personality disorder.

    PubMed

    O'Neill, Aisling; D'Souza, Arun; Samson, Andrea C; Carballedo, Angela; Kerskens, Christian; Frodl, Thomas

    2015-01-30

    Individuals with borderline personality disorder (BPD) commonly display deficits in emotion regulation, but findings in the area of social cognitive (e.g., theory of mind, ToM) capacities have been heterogeneous. The aims of the current study were to investigate differences between patients with BPD and controls in functional connectivity (1) between the emotion and ToM network and (2) in the default mode network (DMN). Functional magnetic resonance imaging was used to investigate 19 healthy controls and 17 patients with BPD at rest and during ToM processing. Functional coupling was analysed. Significantly decreased functional connectivity was found for patients compared with controls between anterior cingulate cortex and three brain areas involved in ToM processes: the left superior temporal lobe, right supramarginal/inferior parietal lobes, and right middle cingulate cortex. Increased functional connectivity was found in patients compared with controls between the precuneus as the DMN seed and the left inferior frontal lobe, left precentral/middle frontal, and left middle occipital/superior parietal lobes during rest. Reduced functional coupling between the emotional and the ToM network during ToM processing is in line with emotion-regulation dysfunctions in BPD. The increased connectivity between precuneus and frontal regions during rest might be related to extensive processing of internal thoughts and self-referential information in BPD. PMID:25482858

  20. Integrated information theory of consciousness: an updated account.

    PubMed

    Tononi, G

    2012-12-01

    This article presents an updated account of integrated information theory of consciousness (liT) and some of its implications. /IT stems from thought experiments that lead to phenomenological axioms (existence, compositionality, information, integration, exclusion) and corresponding ontological postulates. The information axiom asserts that every experience is spec~fic - it is what it is by differing in its particular way from a large repertoire of alternatives. The integration axiom asserts that each experience is unified- it cannot be reduced to independent components. The exclusion axiom asserts that every experience is definite - it is limited to particular things and not others and flows at a particular speed and resolution. /IT formalizes these intuitions with postulates. The information postulate states that only "differences that make a difference" from the intrinsic perpective of a system matter: a mechanism generates cause-effect information if its present state has selective past causes and selective future effects within a system. The integration postulate states that only information that is irreducible matters: mechanisms generate integrated information only to the extent that the information they generate cannot be partitioned into that generated within independent components. The exclusion postulate states that only maxima of integrated information matter: a mechanism specifies only one maximally irreducible set of past causes and future effects - a concept. A complex is a set of elements specifying a maximally irreducible constellation of concepts, where the maximum is evaluated over elements and at the optimal spatiatemporal scale. Its concepts specify a maximally integrated conceptual information structure or quale, which is identical with an experience. Finally, changes in information integration upon exposure to the environment reflect a system's ability to match the causal structure of the world. After introducing an updated definition of

  1. Networking the Land: Rural America in the Information Age.

    ERIC Educational Resources Information Center

    Conte, Christopher

    This report describes 10 projects funded by the federal Technology Opportunities Program, in which people in isolated regions are finding ways to connect to new information networks and are reaping social, economic, and educational benefits. In the sprawling Navajo Nation, where many families lack even basic telephone service, local tribal…

  2. Information Networks for On-Line Bibliographic Retrieval.

    ERIC Educational Resources Information Center

    Karlander, Bo; Sem-Sandberg, Sverre

    This study evaluates the economic and financial aspects of the application of teleprocessing and telecommunications to the transfer of scientific and technological information, especially in the context of developing countries. It was intended to facilitate comparison of the relative value of a teleprocessing network with that of other modes of…

  3. Tufts academic health information network: concept and scenario.

    PubMed

    Stearns, N S

    1986-04-01

    Tufts University School of Medicine's new health sciences education building, the Arthur M. Sackler Center for Health Communications, will house a modern medical library and computer center, classrooms, auditoria, and media facilities. The building will also serve as the center for an information and communication network linking the medical school and adjacent New England Medical Center, Tufts' primary teaching hospital, with Tufts Associated Teaching Hospitals throughout New England. Ultimately, the Tufts network will join other gateway networks, information resource facilities, health care institutions, and medical schools throughout the world. The center and the network are intended to facilitate and improve the education of health professionals, the delivery of health care to patients, the conduct of research, and the implementation of administrative management approaches that should provide more efficient utilization of resources and save dollars. A model and scenario show how health care delivery and health care education are integrated through better use of information transfer technologies by health information specialists, practitioners, and educators. PMID:3708191

  4. ODIN. Online Database Information Network: ODIN Policy & Procedure Manual.

    ERIC Educational Resources Information Center

    Townley, Charles T.; And Others

    Policies and procedures are outlined for the Online Database Information Network (ODIN), a cooperative of libraries in south-central Pennsylvania, which was organized to improve library services through technology. The first section covers organization and goals, members, and responsibilities of the administrative council and libraries. Patrons…

  5. Copyright Aspects of CATV as Utilized in Information Networking.

    ERIC Educational Resources Information Center

    Bachrach, Morton W.

    It can be expected that Cable Antenna Television (CATV) systems will serve as conduits for tomorrow's information networks. CATV holds promise for fulfilling this need because of its broad-band multi-channel capability. CATV can be thought of as having two basic functions. i.e., retransmitting TV programs, and initiating its own programs and…

  6. Installing an Integrated Information System in a Centralized Network.

    ERIC Educational Resources Information Center

    Mendelson, Andrew D.

    1992-01-01

    Many schools are looking at ways to centralize the distribution and retrieval of video, voice, and data transmissions in an integrate information system (IIS). A centralized system offers greater control of hardware and software. Describes media network planning to retrofit an Illinois' high school with a fiber optic-based IIS. (MLF)

  7. Maximizing Information Diffusion in the Cyber-physical Integrated Network.

    PubMed

    Lu, Hongliang; Lv, Shaohe; Jiao, Xianlong; Wang, Xiaodong; Liu, Juan

    2015-01-01

    Nowadays, our living environment has been embedded with smart objects, such as smart sensors, smart watches and smart phones. They make cyberspace and physical space integrated by their abundant abilities of sensing, communication and computation, forming a cyber-physical integrated network. In order to maximize information diffusion in such a network, a group of objects are selected as the forwarding points. To optimize the selection, a minimum connected dominating set (CDS) strategy is adopted. However, existing approaches focus on minimizing the size of the CDS, neglecting an important factor: the weight of links. In this paper, we propose a distributed maximizing the probability of information diffusion (DMPID) algorithm in the cyber-physical integrated network. Unlike previous approaches that only consider the size of CDS selection, DMPID also considers the information spread probability that depends on the weight of links. To weaken the effects of excessively-weighted links, we also present an optimization strategy that can properly balance the two factors. The results of extensive simulation show that DMPID can nearly double the information diffusion probability, while keeping a reasonable size of selection with low overhead in different distributed networks. PMID:26569254

  8. Maximizing Information Diffusion in the Cyber-physical Integrated Network.

    PubMed

    Lu, Hongliang; Lv, Shaohe; Jiao, Xianlong; Wang, Xiaodong; Liu, Juan

    2015-11-11

    Nowadays, our living environment has been embedded with smart objects, such as smart sensors, smart watches and smart phones. They make cyberspace and physical space integrated by their abundant abilities of sensing, communication and computation, forming a cyber-physical integrated network. In order to maximize information diffusion in such a network, a group of objects are selected as the forwarding points. To optimize the selection, a minimum connected dominating set (CDS) strategy is adopted. However, existing approaches focus on minimizing the size of the CDS, neglecting an important factor: the weight of links. In this paper, we propose a distributed maximizing the probability of information diffusion (DMPID) algorithm in the cyber-physical integrated network. Unlike previous approaches that only consider the size of CDS selection, DMPID also considers the information spread probability that depends on the weight of links. To weaken the effects of excessively-weighted links, we also present an optimization strategy that can properly balance the two factors. The results of extensive simulation show that DMPID can nearly double the information diffusion probability, while keeping a reasonable size of selection with low overhead in different distributed networks.

  9. The Implications of a Mixed Media Network for Information Interchange.

    ERIC Educational Resources Information Center

    Meaney, John W.

    A mixed media network for information interchange is what we are always likely to have. Amid the current permutations of the storage and distribution media we see the emergence of two trends -- toward the common denominators of electronic display on the TV system and of digital processing and control. The economic implications of a mixed network…

  10. A Network Client Using the Gopher Information Discovery Protocol

    1993-10-05

    WSGOPHER uses the protocol known as Gopher, which is described in Internet RFC 1436. Specifically Gopher is a client/server protocol. Gopher servers provide information across the network to Gopher clients. WSGOPHER is an implementation of a Gopher client for Microsoft Windows 3.1 and Windows Sockets version 1.1.

  11. Regional Industry Workforce Development: The Gulf Coast Petrochemical Information Network

    ERIC Educational Resources Information Center

    Hodgin, Johnette; Muha, Susan

    2008-01-01

    The Gulf Coast Petrochemical Information Network (GC-PIN) is a workforce development partnership among industry businesses and area institutions of higher education in the four-county Gulf Coast region. GC-PIN partners develop new industry-specific curricula, foster industry career awareness, and retrain existing employees in new technologies.

  12. Audit Trail Management System in Community Health Care Information Network.

    PubMed

    Nakamura, Naoki; Nakayama, Masaharu; Nakaya, Jun; Tominaga, Teiji; Suganuma, Takuo; Shiratori, Norio

    2015-01-01

    After the Great East Japan Earthquake we constructed a community health care information network system. Focusing on the authentication server and portal server capable of SAML&ID-WSF, we proposed an audit trail management system to look over audit events in a comprehensive manner. Through implementation and experimentation, we verified the effectiveness of our proposed audit trail management system.

  13. Fragmentation network of doubly charged methionine: Interpretation using graph theory

    NASA Astrophysics Data System (ADS)

    Ha, D. T.; Yamazaki, K.; Wang, Y.; Alcamí, M.; Maeda, S.; Kono, H.; Martín, F.; Kukk, E.

    2016-09-01

    The fragmentation of doubly charged gas-phase methionine (HO2CCH(NH2)CH2CH2SCH3) is systematically studied using the self-consistent charge density functional tight-binding molecular dynamics (MD) simulation method. We applied graph theory to analyze the large number of the calculated MD trajectories, which appears to be a highly effective and convenient means of extracting versatile information from the large data. The present theoretical results strongly concur with the earlier studied experimental ones. Essentially, the dication dissociates into acidic group CO2H and basic group C4NSH10. The former may carry a single or no charge and stays intact in most cases, whereas the latter may hold either a single or a double charge and tends to dissociate into smaller fragments. The decay of the basic group is observed to follow the Arrhenius law. The dissociation pathways to CO2H and C4NSH10 and subsequent fragmentations are also supported by ab initio calculations.

  14. Fragmentation network of doubly charged methionine: Interpretation using graph theory.

    PubMed

    Ha, D T; Yamazaki, K; Wang, Y; Alcamí, M; Maeda, S; Kono, H; Martín, F; Kukk, E

    2016-09-01

    The fragmentation of doubly charged gas-phase methionine (HO2CCH(NH2)CH2CH2SCH3) is systematically studied using the self-consistent charge density functional tight-binding molecular dynamics (MD) simulation method. We applied graph theory to analyze the large number of the calculated MD trajectories, which appears to be a highly effective and convenient means of extracting versatile information from the large data. The present theoretical results strongly concur with the earlier studied experimental ones. Essentially, the dication dissociates into acidic group CO2H and basic group C4NSH10. The former may carry a single or no charge and stays intact in most cases, whereas the latter may hold either a single or a double charge and tends to dissociate into smaller fragments. The decay of the basic group is observed to follow the Arrhenius law. The dissociation pathways to CO2H and C4NSH10 and subsequent fragmentations are also supported by ab initio calculations. PMID:27608997

  15. Stochastic resonance in ion channels characterized by information theory.

    PubMed

    Goychuk, I; Hänggi, P

    2000-04-01

    We identify a unifying measure for stochastic resonance (SR) in voltage dependent ion channels which comprises periodic (conventional), aperiodic, and nonstationary SR. Within a simplest setting, the gating dynamics is governed by two-state conductance fluctuations, which switch at random time points between two values. The corresponding continuous time point process is analyzed by virtue of information theory. In pursuing this goal we evaluate for our dynamics the tau information, the mutual information, and the rate of information gain. As a main result we find an analytical formula for the rate of information gain that solely involves the probability of the two channel states and their noise averaged rates. For small voltage signals it simplifies to a handy expression. Our findings are applied to study SR in a potassium channel. We find that SR occurs only when the closed state is predominantly dwelled upon. Upon increasing the probability for the open channel state the application of an extra dose of noise monotonically deteriorates the rate of information gain, i.e., no SR behavior occurs.

  16. Estimation of the limit of detection using information theory measures.

    PubMed

    Fonollosa, Jordi; Vergara, Alexander; Huerta, Ramón; Marco, Santiago

    2014-01-31

    Definitions of the limit of detection (LOD) based on the probability of false positive and/or false negative errors have been proposed over the past years. Although such definitions are straightforward and valid for any kind of analytical system, proposed methodologies to estimate the LOD are usually simplified to signals with Gaussian noise. Additionally, there is a general misconception that two systems with the same LOD provide the same amount of information on the source regardless of the prior probability of presenting a blank/analyte sample. Based upon an analogy between an analytical system and a binary communication channel, in this paper we show that the amount of information that can be extracted from an analytical system depends on the probability of presenting the two different possible states. We propose a new definition of LOD utilizing information theory tools that deals with noise of any kind and allows the introduction of prior knowledge easily. Unlike most traditional LOD estimation approaches, the proposed definition is based on the amount of information that the chemical instrumentation system provides on the chemical information source. Our findings indicate that the benchmark of analytical systems based on the ability to provide information about the presence/absence of the analyte (our proposed approach) is a more general and proper framework, while converging to the usual values when dealing with Gaussian noise.

  17. Integrated information theory of consciousness: an updated account.

    PubMed

    Tononi, G

    2012-01-01

    This article presents an updated account of integrated information theory of consciousness (IIT) and some of its implications. IIT stems from thought experiments that lead to phenomenological axioms and ontological postulates. The information axiom asserts that every experience is one out of many, i.e. specific - it is what it is by differing in its particular way from a large repertoire of alternatives. The integration axiom asserts that each experience is one, i.e. unified - it cannot be reduced to independent components. The exclusion axiom asserts that every experience is definite - it is limited to particular things and not others and flows at a particular speed and resolution. IIT formalizes these intuitions with three postulates. The information postulate states that only "differences that make a difference" from the intrinsic perspective of a system matter: a mechanism generates cause-effect information if its present state has specific past causes and specific future effects within a system. The integration postulate states that only information that is irreducible matters: mechanisms generate integrated information only to the extent that the information they generate cannot be partitioned into that generated within independent components. The exclusion postulate states that only maxima of integrated information matter: a mechanism specifies only one maximally irreducible set of past causes and future effects - a concept. A complex is a set of elements specifying a maximally irreducible constellation of concepts, where the maximum is evaluated at the optimal spatio-temporal scale. Its concepts specify a maximally integrated conceptual information structure or quale, which is identical with an experience. Finally, changes in information integration upon exposure to the environment reflect a system's ability to match the causal structure of the world. After introducing an updated definition of information integration and related quantities, the article

  18. A social network model for the development of a 'Theory of Mind'

    NASA Astrophysics Data System (ADS)

    Harré, Michael S.

    2013-02-01

    A "Theory of Mind" is one of the most important skills we as humans have developed; It enables us to infer the mental states and intentions of others, build stable networks of relationships and it plays a central role in our psychological make-up and development. Findings published earlier this year have also shown that we as a species as well as each of us individually benefit from the enlargement of the underlying neuro-anatomical regions that support our social networks, mediated by our Theory of Mind that stabilises these networks. On the basis of such progress and that of earlier work, this paper draws together several different strands from psychology, behavioural economics and network theory in order to generate a novel theoretical representation of the development of our social-cognition and how subsequent larger social networks enables much of our cultural development but at the increased risk of mental disorders.

  19. The resource theory of informational nonequilibrium in thermodynamics

    NASA Astrophysics Data System (ADS)

    Gour, Gilad; Müller, Markus P.; Narasimhachar, Varun; Spekkens, Robert W.; Yunger Halpern, Nicole

    2015-07-01

    We review recent work on the foundations of thermodynamics in the light of quantum information theory. We adopt a resource-theoretic perspective, wherein thermodynamics is formulated as a theory of what agents can achieve under a particular restriction, namely, that the only state preparations and transformations that they can implement for free are those that are thermal at some fixed temperature. States that are out of thermal equilibrium are the resources. We consider the special case of this theory wherein all systems have trivial Hamiltonians (that is, all of their energy levels are degenerate). In this case, the only free operations are those that add noise to the system (or implement a reversible evolution) and the only nonequilibrium states are states of informational nonequilibrium, that is, states that deviate from the maximally mixed state. The degree of this deviation we call the state's nonuniformity; it is the resource of interest here, the fuel that is consumed, for instance, in an erasure operation. We consider the different types of state conversion: exact and approximate, single-shot and asymptotic, catalytic and noncatalytic. In each case, we present the necessary and sufficient conditions for the conversion to be possible for any pair of states, emphasizing a geometrical representation of the conditions in terms of Lorenz curves. We also review the problem of quantifying the nonuniformity of a state, in particular through the use of generalized entropies, and that of quantifying the gap between the nonuniformity one must expend to achieve a single-shot state preparation or state conversion and the nonuniformity one can extract in the reverse operation. Quantum state-conversion problems in this resource theory can be shown to be always reducible to their classical counterparts, so that there are no inherently quantum-mechanical features arising in such problems. This body of work also demonstrates that the standard formulation of the second law

  20. Evaluating reliability and resolution of ensemble forecasts using information theory

    NASA Astrophysics Data System (ADS)

    Weijs, Steven; van de Giesen, Nick

    2010-05-01

    Ensemble forecasts are increasingly popular for the communication of uncertainty towards the public and decision makers. Ideally, an ensemble forecast reflects both the uncertainty and the information in a forecast, which means that the spread in the ensemble should accurately represent the true uncertainty. For ensembles to be useful, they should be probabilistic, as probability is the language to precisely describe an incomplete state of knowledge, that is typical for forecasts. Information theory provides the ideal tools to deal with uncertainty and information in forecasts. Essential to the use and development of models and forecasts are ways to evaluate their quality. Without a proper definition of what is good, it is impossible to improve forecasts. In contrast to forecast value, which is user dependent, forecast quality, which is defined as the correspondence between forecasts and observations, can be objectively defined, given the question that is asked. The evaluation of forecast quality is known as forecast verification. Numerous techniques for forecast verification have been developed over the past decades. The Brier score (BS) and the derived Ranked Probability Score (RPS) are among the most widely used scores for measuring forecast quality. Both of these scores can be split into three additive components: uncertainty, reliability and resolution. While the first component, uncertainty, just depends on the inherent variability in the forecasted event, the latter two measure different aspects of the quality of forecasts themselves. Resolution measures the difference between the conditional probabilities and the marginal probabilities of occurrence. The third component, reliability, measures the conditional bias in the probability estimates, hence unreliability would be a better name. In this work, we argue that information theory should be adopted as the correct framework for measuring quality of probabilistic ensemble forecasts. We use the information

  1. Functional Organization of the Action Observation Network in Autism: A Graph Theory Approach

    PubMed Central

    Alaerts, Kaat; Geerlings, Franca; Herremans, Lynn; Swinnen, Stephan P.; Verhoeven, Judith; Sunaert, Stefan; Wenderoth, Nicole

    2015-01-01

    Background The ability to recognize, understand and interpret other’s actions and emotions has been linked to the mirror system or action-observation-network (AON). Although variations in these abilities are prevalent in the neuro-typical population, persons diagnosed with autism spectrum disorders (ASD) have deficits in the social domain and exhibit alterations in this neural network. Method Here, we examined functional network properties of the AON using graph theory measures and region-to-region functional connectivity analyses of resting-state fMRI-data from adolescents and young adults with ASD and typical controls (TC). Results Overall, our graph theory analyses provided convergent evidence that the network integrity of the AON is altered in ASD, and that reductions in network efficiency relate to reductions in overall network density (i.e., decreased overall connection strength). Compared to TC, individuals with ASD showed significant reductions in network efficiency and increased shortest path lengths and centrality. Importantly, when adjusting for overall differences in network density between ASD and TC groups, participants with ASD continued to display reductions in network integrity, suggesting that also network-level organizational properties of the AON are altered in ASD. Conclusion While differences in empirical connectivity contributed to reductions in network integrity, graph theoretical analyses provided indications that also changes in the high-level network organization reduced integrity of the AON. PMID:26317222

  2. Contextualized Network Analysis: Theory and Methods for Networks with Node Covariates

    NASA Astrophysics Data System (ADS)

    Binkiewicz, Norbert M.

    and rely on molecular markers or the anatomy of a single neuron. These methodologies do not scale. Hence, it is important to investigate the relationship between cell type, as it is currently defined, and other quantities that can be observed or derived at scale. Examining the conditional independence of neuronal cell type and network community structure given neuron location suggests network communities are informative for characterizing neuronal cell type.

  3. Estimating topological properties of weighted networks from limited information

    NASA Astrophysics Data System (ADS)

    Cimini, Giulio; Squartini, Tiziano; Gabrielli, Andrea; Garlaschelli, Diego

    2015-10-01

    A problem typically encountered when studying complex systems is the limitedness of the information available on their topology, which hinders our understanding of their structure and of the dynamical processes taking place on them. A paramount example is provided by financial networks, whose data are privacy protected: Banks publicly disclose only their aggregate exposure towards other banks, keeping individual exposures towards each single bank secret. Yet, the estimation of systemic risk strongly depends on the detailed structure of the interbank network. The resulting challenge is that of using aggregate information to statistically reconstruct a network and correctly predict its higher-order properties. Standard approaches either generate unrealistically dense networks, or fail to reproduce the observed topology by assigning homogeneous link weights. Here, we develop a reconstruction method, based on statistical mechanics concepts, that makes use of the empirical link density in a highly nontrivial way. Technically, our approach consists in the preliminary estimation of node degrees from empirical node strengths and link density, followed by a maximum-entropy inference based on a combination of empirical strengths and estimated degrees. Our method is successfully tested on the international trade network and the interbank money market, and represents a valuable tool for gaining insights on privacy-protected or partially accessible systems.

  4. Estimating topological properties of weighted networks from limited information

    NASA Astrophysics Data System (ADS)

    Gabrielli, Andrea; Cimini, Giulio; Garlaschelli, Diego; Squartini, Angelo

    A typical problem met when studying complex systems is the limited information available on their topology, which hinders our understanding of their structural and dynamical properties. A paramount example is provided by financial networks, whose data are privacy protected. Yet, the estimation of systemic risk strongly depends on the detailed structure of the interbank network. The resulting challenge is that of using aggregate information to statistically reconstruct a network and correctly predict its higher-order properties. Standard approaches either generate unrealistically dense networks, or fail to reproduce the observed topology by assigning homogeneous link weights. Here we develop a reconstruction method, based on statistical mechanics concepts, that exploits the empirical link density in a highly non-trivial way. Technically, our approach consists in the preliminary estimation of node degrees from empirical node strengths and link density, followed by a maximum-entropy inference based on a combination of empirical strengths and estimated degrees. Our method is successfully tested on the international trade network and the interbank money market, and represents a valuable tool for gaining insights on privacy-protected or partially accessible systems. Acknoweledgement to ``Growthcom'' ICT - EC project (Grant No: 611272) and ``Crisislab'' Italian Project.

  5. Intrusion-Tolerant Location Information Services in Intelligent Vehicular Networks

    NASA Astrophysics Data System (ADS)

    Yan, Gongjun; Yang, Weiming; Shaner, Earl F.; Rawat, Danda B.

    Intelligent Vehicular Networks, known as Vehicle-to-Vehicle and Vehicle-to-Roadside wireless communications (also called Vehicular Ad hoc Networks), are revolutionizing our daily driving with better safety and more infortainment. Most, if not all, applications will depend on accurate location information. Thus, it is of importance to provide intrusion-tolerant location information services. In this paper, we describe an adaptive algorithm that detects and filters the false location information injected by intruders. Given a noisy environment of mobile vehicles, the algorithm estimates the high resolution location of a vehicle by refining low resolution location input. We also investigate results of simulations and evaluate the quality of the intrusion-tolerant location service.

  6. Two theories of consciousness: Semantic pointer competition vs. information integration.

    PubMed

    Thagard, Paul; Stewart, Terrence C

    2014-11-01

    Consciousness results from three mechanisms: representation by firing patterns in neural populations, binding of representations into more complex representations called semantic pointers, and competition among semantic pointers to capture the most important aspects of an organism's current state. We contrast the semantic pointer competition (SPC) theory of consciousness with the hypothesis that consciousness is the capacity of a system to integrate information (IIT). We describe computer simulations to show that SPC surpasses IIT in providing better explanations of key aspects of consciousness: qualitative features, onset and cessation, shifts in experiences, differences in kinds across different organisms, unity and diversity, and storage and retrieval.

  7. Impact of Repeated Exposures on Information Spreading in Social Networks.

    PubMed

    Zhou, Cangqi; Zhao, Qianchuan; Lu, Wenbo

    2015-01-01

    Clustered structure of social networks provides the chances of repeated exposures to carriers with similar information. It is commonly believed that the impact of repeated exposures on the spreading of information is nontrivial. Does this effect increase the probability that an individual forwards a message in social networks? If so, to what extent does this effect influence people's decisions on whether or not to spread information? Based on a large-scale microblogging data set, which logs the message spreading processes and users' forwarding activities, we conduct a data-driven analysis to explore the answer to the above questions. The results show that an overwhelming majority of message samples are more probable to be forwarded under repeated exposures, compared to those under only a single exposure. For those message samples that cover various topics, we observe a relatively fixed, topic-independent multiplier of the willingness of spreading when repeated exposures occur, regardless of the differences in network structure. We believe that this finding reflects average people's intrinsic psychological gain under repeated stimuli. Hence, it makes sense that the gain is associated with personal response behavior, rather than network structure. Moreover, we find that the gain is robust against the change of message popularity. This finding supports that there exists a relatively fixed gain brought by repeated exposures. Based on the above findings, we propose a parsimonious model to predict the saturated numbers of forwarding activities of messages. Our work could contribute to better understandings of behavioral psychology and social media analytics. PMID:26465749

  8. Part mutual information for quantifying direct associations in networks.

    PubMed

    Zhao, Juan; Zhou, Yiwei; Zhang, Xiujun; Chen, Luonan

    2016-05-01

    Quantitatively identifying direct dependencies between variables is an important task in data analysis, in particular for reconstructing various types of networks and causal relations in science and engineering. One of the most widely used criteria is partial correlation, but it can only measure linearly direct association and miss nonlinear associations. However, based on conditional independence, conditional mutual information (CMI) is able to quantify nonlinearly direct relationships among variables from the observed data, superior to linear measures, but suffers from a serious problem of underestimation, in particular for those variables with tight associations in a network, which severely limits its applications. In this work, we propose a new concept, "partial independence," with a new measure, "part mutual information" (PMI), which not only can overcome the problem of CMI but also retains the quantification properties of both mutual information (MI) and CMI. Specifically, we first defined PMI to measure nonlinearly direct dependencies between variables and then derived its relations with MI and CMI. Finally, we used a number of simulated data as benchmark examples to numerically demonstrate PMI features and further real gene expression data from Escherichia coli and yeast to reconstruct gene regulatory networks, which all validated the advantages of PMI for accurately quantifying nonlinearly direct associations in networks.

  9. Impact of Repeated Exposures on Information Spreading in Social Networks.

    PubMed

    Zhou, Cangqi; Zhao, Qianchuan; Lu, Wenbo

    2015-01-01

    Clustered structure of social networks provides the chances of repeated exposures to carriers with similar information. It is commonly believed that the impact of repeated exposures on the spreading of information is nontrivial. Does this effect increase the probability that an individual forwards a message in social networks? If so, to what extent does this effect influence people's decisions on whether or not to spread information? Based on a large-scale microblogging data set, which logs the message spreading processes and users' forwarding activities, we conduct a data-driven analysis to explore the answer to the above questions. The results show that an overwhelming majority of message samples are more probable to be forwarded under repeated exposures, compared to those under only a single exposure. For those message samples that cover various topics, we observe a relatively fixed, topic-independent multiplier of the willingness of spreading when repeated exposures occur, regardless of the differences in network structure. We believe that this finding reflects average people's intrinsic psychological gain under repeated stimuli. Hence, it makes sense that the gain is associated with personal response behavior, rather than network structure. Moreover, we find that the gain is robust against the change of message popularity. This finding supports that there exists a relatively fixed gain brought by repeated exposures. Based on the above findings, we propose a parsimonious model to predict the saturated numbers of forwarding activities of messages. Our work could contribute to better understandings of behavioral psychology and social media analytics.

  10. Rich club neurons dominate Information Transfer in local cortical networks

    NASA Astrophysics Data System (ADS)

    Nigam, Sunny; Shimono, Masanori; Sporns, Olaf; Beggs, John

    2015-03-01

    The performance of complex networks depends on how they route their traffic. It is unknown how information is transferred in local cortical networks of hundreds of closely-spaced neurons. To address this, it is necessary to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512 electrode array (60 μm spacing) to record spontaneous activity at 20 kHz, simultaneously from up to 700 neurons in slice cultures of mouse somatosensory cortex for 1 hr at a time. We used transfer entropy to quantify directed information transfer (IT) between pairs of neurons. We found an approximately lognormal distribution of firing rates as reported in in-vivo. Pairwise information transfer strengths also were nearly lognormally distributed, similar to synaptic strengths. 20% of the neurons accounted for 70% of the total IT coming into, and going out of the network and were defined as rich nodes. These rich nodes were more densely and strongly connected to each other expected by chance, forming a rich club. This highly uneven distribution of IT has implications for the efficiency and robustness of local cortical networks, and gives clues to the plastic processes that shape them. JSPS.

  11. Part mutual information for quantifying direct associations in networks.

    PubMed

    Zhao, Juan; Zhou, Yiwei; Zhang, Xiujun; Chen, Luonan

    2016-05-01

    Quantitatively identifying direct dependencies between variables is an important task in data analysis, in particular for reconstructing various types of networks and causal relations in science and engineering. One of the most widely used criteria is partial correlation, but it can only measure linearly direct association and miss nonlinear associations. However, based on conditional independence, conditional mutual information (CMI) is able to quantify nonlinearly direct relationships among variables from the observed data, superior to linear measures, but suffers from a serious problem of underestimation, in particular for those variables with tight associations in a network, which severely limits its applications. In this work, we propose a new concept, "partial independence," with a new measure, "part mutual information" (PMI), which not only can overcome the problem of CMI but also retains the quantification properties of both mutual information (MI) and CMI. Specifically, we first defined PMI to measure nonlinearly direct dependencies between variables and then derived its relations with MI and CMI. Finally, we used a number of simulated data as benchmark examples to numerically demonstrate PMI features and further real gene expression data from Escherichia coli and yeast to reconstruct gene regulatory networks, which all validated the advantages of PMI for accurately quantifying nonlinearly direct associations in networks. PMID:27092000

  12. Information Exchange and Information Disclosure in Social Networking Web Sites: Mediating Role of Trust

    ERIC Educational Resources Information Center

    Mital, Monika; Israel, D.; Agarwal, Shailja

    2010-01-01

    Purpose: The purpose of this paper is to examine the mediating effect of trust on the relationship between the type of information exchange (IE) and information disclosure (ID) on social networking web sites (SNWs). Design/methodology/approach: Constructs were developed for type of IE and trust. To understand the mediating role of trust a…

  13. DELIVERING TIMELY ENVIRONMENTAL INFORMATION TO YOUR COMMUNITY: THE BOULDER AREA SUSTAINABILITY INFORMATION NETWORK: OTHER

    EPA Science Inventory

    NRMRL-CIN-1577 Petersen*, D., Barber, L., Dilworth, G, Fiebelkorn, T., McCaffrey, M., Murphy, S., Rudkin, C., Scott, D., and Waterman, J. Delivering Timely Environmental Information to your Community: The Boulder Area Sustainability Information Network. EPA/625/C-01/010. The Te...

  14. Conceptual Framework for Developing a Diabetes Information Network

    PubMed Central

    Riazi, Hossein; Langarizadeh, Mostafa; Larijani, Bagher; Shahmoradi, Leila

    2016-01-01

    Objective: To provide a conceptual framework for managing diabetic patient care, and creating an information network for clinical research. Background: A wide range of information technology (IT) based interventions such as distance learning, diabetes registries, personal or electronic health record systems, clinical information systems, and clinical decision support systems have so far been used in supporting diabetic care. Previous studies demonstrated that IT could improve diabetes care at its different aspects. There is however no comprehensive conceptual framework that defines how different IT applications can support diverse aspects of this care. Therefore, a conceptual framework that combines different IT solutions into a wide information network for improving care processes and for research purposes is widely lacking. In this study we describe the theoretical underpin of a big project aiming at building a wide diabetic information network namely DIANET. Research design and methods: A literature review and a survey of national programs and existing regulations for diabetes management was conducted in order to define different aspects of diabetic care that should be supported by IT solutions. Both qualitative and quantitative research methods were used in this study. In addition to the results of a previous systematic literature review, two brainstorming and three expert panel sessions were conducted to identify requirements of a comprehensive information technology solution. Based on these inputs, the requirements for creating a diabetes information network were identified and used to create a questionnaire based on 9-point Likert scale. The questionnaire was finalized after removing some items based on calculated content validity ratio and content validity index coefficients. Cronbach’s alpha reliability coefficient was also calculated (αTotal= 0.98, P<0.05, CI=0.95). The final questionnaire was containing 45 items. It was sent to 13 clinicians at two

  15. The trouble with CHINS (community health information networks).

    PubMed

    Appleby, C

    1995-05-01

    If one had to choose a single acronym that captures both the vision and folly of today's health care industry, it would be "CHIN." The term--which stands for "community health information network"--combines the idealism of community-based health care with the promise of computer automation. Ironically, the acronym may come to be synonymous with "sticking your neck out"; these costly computer networks presuppose a quixotic collaboration among hospitals and health systems long at one another's competitive throats. For CHINs to succeed, they must overcome barriers that many industry insiders say are insurmountable.

  16. The Deep Space Network information system in the year 2000

    NASA Technical Reports Server (NTRS)

    Markley, R. W.; Beswick, C. A.

    1992-01-01

    The Deep Space Network (DSN), the largest, most sensitive scientific communications and radio navigation network in the world, is considered. Focus is made on the telemetry processing, monitor and control, and ground data transport architectures of the DSN ground information system envisioned for the year 2000. The telemetry architecture will be unified from the front-end area to the end user. It will provide highly automated monitor and control of the DSN, automated configuration of support activities, and a vastly improved human interface. Automated decision support systems will be in place for DSN resource management, performance analysis, fault diagnosis, and contingency management.

  17. A Brief Historical Introduction to Euler's Formula for Polyhedra, Topology, Graph Theory and Networks

    ERIC Educational Resources Information Center

    Debnath, Lokenath

    2010-01-01

    This article is essentially devoted to a brief historical introduction to Euler's formula for polyhedra, topology, theory of graphs and networks with many examples from the real-world. Celebrated Konigsberg seven-bridge problem and some of the basic properties of graphs and networks for some understanding of the macroscopic behaviour of real…

  18. A New Kind of Symmetry: Actor-Network Theories and the New Literacy Studies.

    ERIC Educational Resources Information Center

    Clarke, Julia

    2002-01-01

    Discusses concepts and assumptions underlying actor-network theories and the new literacy studies. Identifies lessons for adult education research derived from sociological approaches to scientific knowledge, which examine power circulating in networks of human and nonhuman entities. (Contains 36 references.) (SK)

  19. A Social Network Perspective on Teacher Collaboration in Schools: Theory, Methodology, and Applications

    ERIC Educational Resources Information Center

    Moolenaar, Nienke M.

    2012-01-01

    An emerging trend in educational research is the use of social network theory and methodology to understand how teacher collaboration can support or constrain teaching, learning, and educational change. This article provides a critical synthesis of educational literature on school social networks among educators to advance our understanding of the…

  20. A brief historical introduction to Euler's formula for polyhedra, topology, graph theory and networks

    NASA Astrophysics Data System (ADS)

    Debnath, Lokenath

    2010-09-01

    This article is essentially devoted to a brief historical introduction to Euler's formula for polyhedra, topology, theory of graphs and networks with many examples from the real-world. Celebrated Königsberg seven-bridge problem and some of the basic properties of graphs and networks for some understanding of the macroscopic behaviour of real physical systems are included. We also mention some important and modern applications of graph theory or network problems from transportation to telecommunications. Graphs or networks are effectively used as powerful tools in industrial, electrical and civil engineering, communication networks in the planning of business and industry. Graph theory and combinatorics can be used to understand the changes that occur in many large and complex scientific, technical and medical systems. With the advent of fast large computers and the ubiquitous Internet consisting of a very large network of computers, large-scale complex optimization problems can be modelled in terms of graphs or networks and then solved by algorithms available in graph theory. Many large and more complex combinatorial problems dealing with the possible arrangements of situations of various kinds, and computing the number and properties of such arrangements can be formulated in terms of networks. The Knight's tour problem, Hamilton's tour problem, problem of magic squares, the Euler Graeco-Latin squares problem and their modern developments in the twentieth century are also included.